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Kumar D, Li H, Kumbhar DD, Rajbhar MK, Das UK, Syed AM, Melinte G, El-Atab N. Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision. NANO-MICRO LETTERS 2024; 16:238. [PMID: 38976105 PMCID: PMC11231128 DOI: 10.1007/s40820-024-01456-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024]
Abstract
The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.
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Affiliation(s)
- Dayanand Kumar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Hanrui Li
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Dhananjay D Kumbhar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Manoj Kumar Rajbhar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Uttam Kumar Das
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Abdul Momin Syed
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Georgian Melinte
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Nazek El-Atab
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia.
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2
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Guo B, Zhong X, Yu Z, He Z, Liu S, Wu Z, Liu S, Guo Y, Chen W, Duan H, Zeng J, Gao P, Zhang B, Chen Q, He H, Chen Y, Liu G. Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation. MATERIALS HORIZONS 2024. [PMID: 38953878 DOI: 10.1039/d3mh01950k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.
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Affiliation(s)
- Bingjie Guo
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaolong Zhong
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhe Yu
- School of Materials, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Zhilong He
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Shuzhi Liu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Zhixin Wu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sixian Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanbo Guo
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weilin Chen
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hongxiao Duan
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jianmin Zeng
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Pingqi Gao
- School of Materials, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
| | - Bin Zhang
- School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Qian Chen
- School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
- Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Haidong He
- Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Yu Chen
- School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Gang Liu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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3
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Woo KS, Park H, Ghenzi N, Talin AA, Jeong T, Choi JH, Oh S, Jang YH, Han J, Williams RS, Kumar S, Hwang CS. Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing. ACS NANO 2024; 18:17007-17017. [PMID: 38952324 DOI: 10.1021/acsnano.4c03238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.
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Affiliation(s)
- Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Hyungjun Park
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Nestor Ghenzi
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Universidad de Avellaneda UNDAV and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mario Bravo 1460, Avellaneda, Buenos Aires 1872, Argentina
| | - A Alec Talin
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Taeyoung Jeong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Jung-Hae Choi
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Sangheon Oh
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - R Stanley Williams
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
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Wang H, Yang J, Yang Z, Liu G, Tang Y, Shao Y, Yan X. Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2403150. [PMID: 38952052 DOI: 10.1002/advs.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/17/2024] [Indexed: 07/03/2024]
Abstract
Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.
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Affiliation(s)
- Hong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Jialiang Yang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Zheng Yang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Gongjie Liu
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Yusong Tang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Yiduo Shao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, 071002, China
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117576, Singapore
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5
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Wang Z, Wan T, Ma S, Chai Y. Multidimensional vision sensors for information processing. NATURE NANOTECHNOLOGY 2024:10.1038/s41565-024-01665-7. [PMID: 38877323 DOI: 10.1038/s41565-024-01665-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/07/2024] [Indexed: 06/16/2024]
Abstract
The visual scene in the physical world integrates multidimensional information (spatial, temporal, polarization, spectrum and so on) and typically shows unstructured characteristics. Conventional image sensors cannot process this multidimensional vision data, creating a need for vision sensors that can efficiently extract features from substantial multidimensional vision data. Vision sensors are able to transform the unstructured visual scene into featured information without relying on sophisticated algorithms and complex hardware. The response characteristics of sensors can be abstracted into operators with specific functionalities, allowing for the efficient processing of perceptual information. In this Review, we delve into the hardware implementation of multidimensional vision sensors, exploring their working mechanisms and design principles. We exemplify multidimensional vision sensors built on emerging devices and silicon-based system integration. We further provide benchmarking metrics for multidimensional vision sensors and conclude with the principle of device-system co-design and co-optimization.
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Affiliation(s)
- Zhaoqing Wang
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Sijie Ma
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
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6
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Bae J, Kwon C, Park SO, Jeong H, Park T, Jang T, Cho Y, Kim S, Choi S. Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems. SCIENCE ADVANCES 2024; 10:eadm7221. [PMID: 38848362 PMCID: PMC11160469 DOI: 10.1126/sciadv.adm7221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO2-x memristors, we experimentally demonstrate reduced device variations, improved switching speeds, and enhanced switching windows. Atomistic simulations of amorphous TiO2-x reveal that fluoride ions attract oxygen vacancies, improving the reversible redistribution and uniformity. A number of migration barrier calculations statistically show that fluoride ions also reduce the migration energies of nearby oxygen vacancies, facilitating ionic diffusion and high-speed switching. The detailed Voronoi volume analysis further suggests design principles in terms of the migrating species' electrostatic repulsion and migration barriers. This work presents an innovative methodology for the fabrication of reliable memristor devices, contributing to the realization of hardware-based neuromorphic systems.
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Affiliation(s)
- Jongmin Bae
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Choah Kwon
- Department of Nuclear Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - See-On Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hakcheon Jeong
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Taehoon Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Taehwan Jang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yoonho Cho
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Sangtae Kim
- Department of Nuclear Engineering, Hanyang University, Seoul 04763, Republic of Korea
- Department of Material Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Shinhyun Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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7
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Liu X, Dai S, Zhao W, Zhang J, Guo Z, Wu Y, Xu Y, Sun T, Li L, Guo P, Yang J, Hu H, Zhou J, Zhou P, Huang J. All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312473. [PMID: 38385598 DOI: 10.1002/adma.202312473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.
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Affiliation(s)
- Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Shilei Dai
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Weidong Zhao
- School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jie Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Huawei Hu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, P. R. China
| | - Junhe Zhou
- School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Peng Zhou
- State Key Laboratory of ASIC and System, School of Microelectronics, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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8
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Xu B, Guo D, Dong W, Gao H, Zhu P, Wang Z, Watanabe K, Taniguchi T, Luo Z, Zheng F, Zheng S, Zhou J. Gap State-Modulated Van Der Waals Short-Term Memory with Broad Band Negative Photoconductance. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309626. [PMID: 38098431 DOI: 10.1002/smll.202309626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/05/2023] [Indexed: 05/25/2024]
Abstract
Floating gate memory (FGM), composed of van der Waals (vdW) junctions with an atomically thin floating layer for charge storage, is widely employed to develop logic-in memories and in-sensor computing devices. Most research efforts of FGM are spent on achieving long-term charge storage and fast reading/writing for flash and random-access memory. However, dynamic modulation of memory time via a tunneling barrier and vdW interfaces, which is critical for synaptic computing and machine vision, is still lacking. Here, a van der Waals short-term memory with tunable memory windows and retention times from milliseconds to thousands of seconds is reported, which is approximately exponentially proportional to the thickness h-BN (hexagonal boron nitride) barrier. The specific h-BN barrier with fruitful gap states provides charge release channels for trapped charges, making the vdW device switchable between positive photoconductance and negative photoconductance with a broadband light from IR to UV range. The dynamic short-term memory with tunable photo response highlights the design strategy of novel vdW memory vis interface engineering for further intelligent information storage and optoelectronic detection.
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Affiliation(s)
- Boyu Xu
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Dan Guo
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Weikang Dong
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Huiying Gao
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Peng Zhu
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhiwei Wang
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Kenji Watanabe
- National Institute for Materials Science, 1-1 Namiki, Tsukuba, 303-0044, Japan
| | - Takashi Taniguchi
- National Institute for Materials Science, 1-1 Namiki, Tsukuba, 303-0044, Japan
| | - Zhaochu Luo
- State Key Laboratory of Artificial Microstructure and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Fawei Zheng
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Shoujun Zheng
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jiadong Zhou
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
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9
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Li L, Xiang H, Zheng H, Chien YC, Duong NT, Gao J, Ang KW. Physical reservoirs based on MoS 2-HZO integrated ferroelectric field-effect transistors for reservoir computing systems. NANOSCALE HORIZONS 2024; 9:752-763. [PMID: 38465422 DOI: 10.1039/d3nh00524k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO2-based ferroelectric materials such as Hf0.5Zr0.5O2 (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS2-HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.
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Affiliation(s)
- Lingqi Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Heng Xiang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Haofei Zheng
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Ngoc Thanh Duong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Jing Gao
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
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10
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Chen X, Yang D, Hwang G, Dong Y, Cui B, Wang D, Chen H, Lin N, Zhang W, Li H, Shao R, Lin P, Hong H, Yao Y, Sun L, Wang Z, Yang H. Oscillatory Neural Network-Based Ising Machine Using 2D Memristors. ACS NANO 2024; 18:10758-10767. [PMID: 38598699 DOI: 10.1021/acsnano.3c10559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.
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Affiliation(s)
- Xi Chen
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dongliang Yang
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Geunwoo Hwang
- Division of Chemical Engineering and Materials Science, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Yujiao Dong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Binbin Cui
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dingchen Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Hegan Chen
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ning Lin
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Wenqi Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Huihan Li
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Ruiwen Shao
- Beijing Advanced Innovation Center for Intelligent Robots and Systems and Institute of Engineering Medicine, Beijing Institute of Technology, Beijing 100081, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Hang Zhou 310013, China
| | - Heemyoung Hong
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Yugui Yao
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Linfeng Sun
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Heejun Yang
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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11
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Li P, Zhang M, Zhou Q, Zhang Q, Xie D, Li G, Liu Z, Wang Z, Guo E, He M, Wang C, Gu L, Yang G, Jin K, Ge C. Reconfigurable optoelectronic transistors for multimodal recognition. Nat Commun 2024; 15:3257. [PMID: 38627413 PMCID: PMC11021444 DOI: 10.1038/s41467-024-47580-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.
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Affiliation(s)
- Pengzhan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing, China
| | - Mingzhen Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Qingli Zhou
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing, China
| | - Qinghua Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Yangtze River Delta Physics Research Center Co. Ltd., Liyang, China
| | - Donggang Xie
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Ge Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Zhuohui Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Erjia Guo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Meng He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Lin Gu
- Beijing National Center for Electron Microscopy and Laboratory of Advanced Materials, Department of Materials Science and Engineering, Tsinghua University, Beijing, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China.
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China.
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12
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Carrara S, Chen J, Bhardwaj K, Golparvar A, Barbruni GL. In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:361-368. [PMID: 38015674 DOI: 10.1109/tbcas.2023.3334144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.
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13
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Franco M, Kiazadeh A, Deuermeier J, Lanceros-Méndez S, Martins R, Carlos E. Inkjet printed IGZO memristors with volatile and non-volatile switching. Sci Rep 2024; 14:7469. [PMID: 38553556 PMCID: PMC10980760 DOI: 10.1038/s41598-024-58228-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Solution-based memristors deposited by inkjet printing technique have a strong technological potential based on their scalability, low cost, environmentally friendlier processing by being an efficient technique with minimal material waste. Indium-gallium-zinc oxide (IGZO), an oxide semiconductor material, shows promising resistive switching properties. In this work, a printed Ag/IGZO/ITO memristor has been fabricated. The IGZO thickness influences both memory window and switching voltage of the devices. The devices show both volatile counter8wise (c8w) and non-volatile 8wise (8w) switching at low operating voltage. The 8w switching has a SET and RESET voltage lower than 2 V and - 5 V, respectively, a retention up to 105 s and a memory window up to 100, whereas the c8w switching shows volatile characteristics with a low threshold voltage (Vth < - 0.65 V) and a characteristic time (τ) of 0.75 ± 0.12 ms when a single pulse of - 0.65 V with width of 0.1 ms is applied. The characteristic time alters depending on the number of pulses. These volatile characteristics allowed them to be tested on different 4-bit pulse sequences, as an initial proof of concept for temporal signal processing applications.
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Affiliation(s)
- Miguel Franco
- Center of Physics, University of Minho and Laboratory of Physics for Materials and Emergent Technologies, LapMET, Campus de Gualtar, 4710-057, Braga, Portugal
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - Asal Kiazadeh
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal.
| | - Jonas Deuermeier
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - S Lanceros-Méndez
- Center of Physics, University of Minho and Laboratory of Physics for Materials and Emergent Technologies, LapMET, Campus de Gualtar, 4710-057, Braga, Portugal
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
| | - Rodrigo Martins
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - Emanuel Carlos
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal.
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14
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Shi T, Zhang H, Cui S, Liu J, Gu Z, Wang Z, Yan X, Liu Q. Stochastic neuro-fuzzy system implemented in memristor crossbar arrays. SCIENCE ADVANCES 2024; 10:eadl3135. [PMID: 38517972 PMCID: PMC10959402 DOI: 10.1126/sciadv.adl3135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024]
Abstract
Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.
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Affiliation(s)
- Tuo Shi
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Hui Zhang
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Shiyu Cui
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Jinchang Liu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zixi Gu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zhanfeng Wang
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Xiaobing Yan
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Qi Liu
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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15
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Rani A, Sultan MJ, Ren W, Bag A, Lee HJ, Lee NE, Kim TG. Bio-Inspired Photosensory Artificial Synapse Based on Functionalized Tellurium Multiropes for Neuromorphic Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2310013. [PMID: 38477696 DOI: 10.1002/smll.202310013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/07/2024] [Indexed: 03/14/2024]
Abstract
Nanomaterials like graphene and transition metal dichalcogenides are being explored for developing artificial photosensory synapses with low-power optical plasticity and high retention time for practical nervous system implementation. However, few studies are conducted on Tellurium (Te)-based nanomaterials due to their direct and small bandgaps. This paper reports the superior photo-synaptic properties of covalently bonded Tellurium sulfur oxide (TeSOx ) and Tellurium selenium oxide (TeSeOx )nanomaterials, which are fabricated by incorporating S and Se atoms on the surface of Te multiropes using vapor deposition. Unlike pure Te multiropes, the TeSOx and TeSeOx multiropes exhibit controllable temporal dynamics under optical stimulation. For example, the TeSOx multirope-based transistor displays a photosensory synaptic response to UV light (λ = 365 nm). Furthermore, the TeSeOx multirope-based transistor exhibits photosensory synaptic responses to UV-vis light (λ = 365, 565, and 660 nm), reliable electrical performance, and a combination of both photodetector and optical artificial synaptic properties with a maximum responsivity of 1500 AW-1 to 365 nm UV light. This result is among the highest reported for Te-heterostructure-based devices, enabling optical artificial synaptic applications with low voltage spikes (1 V) and low light intensity (21 µW cm-2 ), potentially useful for optical neuromorphic computing.
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Affiliation(s)
- Adila Rani
- Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Wanqi Ren
- Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Ho Jin Lee
- Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tae Geun Kim
- Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea
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16
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Choi S, Shin J, Park G, Eo JS, Jang J, Yang JJ, Wang G. 3D-integrated multilayered physical reservoir array for learning and forecasting time-series information. Nat Commun 2024; 15:2044. [PMID: 38448419 PMCID: PMC10917743 DOI: 10.1038/s41467-024-46323-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/22/2024] [Indexed: 03/08/2024] Open
Abstract
A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.
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Affiliation(s)
- Sanghyeon Choi
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA
| | - Jaeho Shin
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Gwanyeong Park
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Sun Eo
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jingon Jang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- School of Computer and Information Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
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17
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Yan M, Huang C, Bienstman P, Tino P, Lin W, Sun J. Emerging opportunities and challenges for the future of reservoir computing. Nat Commun 2024; 15:2056. [PMID: 38448438 PMCID: PMC10917819 DOI: 10.1038/s41467-024-45187-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 01/16/2024] [Indexed: 03/08/2024] Open
Abstract
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.
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Affiliation(s)
- Min Yan
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China
| | - Can Huang
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China.
| | - Peter Bienstman
- Photonics Research Group, Department of Information Technology, Ghent University, Gent, Belgium
| | - Peter Tino
- School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China
- School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University, Shanghai, 200433, China
| | - Jie Sun
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China.
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18
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Duong NT, Shi Y, Li S, Chien YC, Xiang H, Zheng H, Li P, Li L, Wu Y, Ang KW. Coupled Ferroelectric-Photonic Memory in a Retinomorphic Hardware for In-Sensor Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2303447. [PMID: 38234245 DOI: 10.1002/advs.202303447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/14/2023] [Indexed: 01/19/2024]
Abstract
The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.
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Affiliation(s)
- Ngoc Thanh Duong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yufei Shi
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Heng Xiang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Haofei Zheng
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Peiyang Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Lingqi Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yangwu Wu
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
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19
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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20
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Zhou H, Li S, Ang KW, Zhang YW. Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials. NANO-MICRO LETTERS 2024; 16:121. [PMID: 38372805 PMCID: PMC10876512 DOI: 10.1007/s40820-024-01335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
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Affiliation(s)
- Hangbo Zhou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore.
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Republic of Singapore.
| | - Yong-Wei Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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21
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Qi M, Xu R, Ding G, Zhou K, Zhu S, Leng Y, Sun T, Zhou Y, Han ST. An in-sensor humidity computing system for contactless human-computer interaction. MATERIALS HORIZONS 2024; 11:939-948. [PMID: 38078356 DOI: 10.1039/d3mh01734f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.
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Affiliation(s)
- Meng Qi
- Institute for Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Runze Xu
- Institute for Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Kui Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Shirui Zhu
- Institute for Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Yanbing Leng
- Institute for Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Tao Sun
- Institute for Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong, China.
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22
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Chen R, Yang H, Li R, Yu G, Zhang Y, Dong J, Han D, Zhou Z, Huang P, Liu L, Liu X, Kang J. Thin-film transistor for temporal self-adaptive reservoir computing with closed-loop architecture. SCIENCE ADVANCES 2024; 10:eadl1299. [PMID: 38363846 PMCID: PMC10871528 DOI: 10.1126/sciadv.adl1299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
Reservoir computing is a powerful neural network-based computing paradigm for spatiotemporal signal processing. Recently, physical reservoirs have been explored based on various electronic devices with outstanding efficiency. However, the inflexible temporal dynamics of these reservoirs have posed fundamental restrictions in processing spatiotemporal signals with various timescales. Here, we fabricated thin-film transistors with controllable temporal dynamics, which can be easily tuned with electrical operation signals and showed excellent cycle-to-cycle uniformity. Based on this, we constructed a temporal adaptive reservoir capable of extracting temporal information of multiple timescales, thereby achieving improved accuracy in the human-activity-recognition task. Moreover, by leveraging the former computing output to modify the hyperparameters, we constructed a closed-loop architecture that equips the reservoir computing system with temporal self-adaptability according to the current input. The adaptability is demonstrated by accurate real-time recognition of objects moving at diverse speed levels. This work provides an approach for reservoir computing systems to achieve real-time processing of spatiotemporal signals with compound temporal characteristics.
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Affiliation(s)
- Ruiqi Chen
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Haozhang Yang
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Ruiyi Li
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Guihai Yu
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Yizhou Zhang
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Junchen Dong
- Beijing Information Science and Technology University, Beijing 100192, China
| | - Dedong Han
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Zheng Zhou
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Peng Huang
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Lifeng Liu
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Xiaoyan Liu
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
| | - Jinfeng Kang
- School of Integrated Circuits, Peking University, Beijing 100871, China
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China
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23
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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24
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Wang J, Pan X, Zhao Z, Xie Y, Luo W, Xie Q, Zeng H, Shuai Y, Song Z, Wu C, Zhang W. An Infrared Near-Sensor Reservoir Computing System Based on Large-Dynamic-Space Memristor with Tens of Thousands of States for Dynamic Gesture Perception. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307359. [PMID: 38145361 PMCID: PMC10853732 DOI: 10.1002/advs.202307359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/21/2023] [Indexed: 12/26/2023]
Abstract
To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.
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Affiliation(s)
- Jiejun Wang
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Xinqiang Pan
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Zebin Zhao
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Yiduo Xie
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Wenbo Luo
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Qin Xie
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Huizhong Zeng
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Yao Shuai
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Zeqian Song
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Chuangui Wu
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Wanli Zhang
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
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25
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Gao C, Liu D, Xu C, Xie W, Zhang X, Bai J, Lin Z, Zhang C, Hu Y, Guo T, Chen H. Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction. Nat Commun 2024; 15:740. [PMID: 38272878 PMCID: PMC10810880 DOI: 10.1038/s41467-024-44942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.
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Affiliation(s)
- Changsong Gao
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Di Liu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Chenhui Xu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Weidong Xie
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Xianghong Zhang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Junhua Bai
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, 350207, Fuzhou, China
| | - Zhixian Lin
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- School of Advanced Manufacturing, Fuzhou University, 362200, Quanzhou, China
| | - Cheng Zhang
- Department of Physics, Fuzhou University, 350108, Fuzhou, China
| | - Yuanyuan Hu
- Changsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, 410082, Changsha, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China.
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26
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Jang YH, Han JK, Moon S, Shim SK, Han J, Cheong S, Lee SH, Hwang CS. A high-dimensional in-sensor reservoir computing system with optoelectronic memristors for high-performance neuromorphic machine vision. MATERIALS HORIZONS 2024; 11:499-509. [PMID: 37966888 DOI: 10.1039/d3mh01584j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
In-sensor reservoir computing (RC) is a promising technology to reduce power consumption and training costs of machine vision systems by processing optical signals temporally. This study demonstrates a high-dimensional in-sensor RC system with optoelectronic memristors to enhance the performance of the in-sensor RC system. Because optoelectronic memristors can respond to both optical and electrical stimuli, optical and electrical masks are proposed to improve the dimensionality and performance of the in-sensor RC system. An optical mask is employed to regulate the wavelength of light, while an electrical mask is used to control the initial conductance of zinc oxide optoelectronic memristors. The distinct characteristics of these two masks contribute to the representation of various distinguishable reservoir states, making it possible to implement diverse reservoir configurations with minimal correlation and to increase the dimensionality of the in-sensor RC system. Using the high-dimensional in-sensor RC system, handwritten digits are successfully classified with an accuracy of 94.1%. Furthermore, human action pattern recognition is achieved with a high accuracy of 99.4%. These high accuracies are achieved with the use of a single-layer readout network, which can significantly reduce the network size and training costs.
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Affiliation(s)
- Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Joon-Kyu Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sangik Moon
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sung Keun Shim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunwoo Cheong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Soo Hyung Lee
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
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Zha J, Xia Y, Shi S, Huang H, Li S, Qian C, Wang H, Yang P, Zhang Z, Meng Y, Wang W, Yang Z, Yu H, Ho JC, Wang Z, Tan C. A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308502. [PMID: 37862005 DOI: 10.1002/adma.202308502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Indexed: 10/21/2023]
Abstract
The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.
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Affiliation(s)
- Jiajia Zha
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Yunpeng Xia
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Shuhui Shi
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, 999077, China
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Haoxin Huang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Siyuan Li
- Department of Chemistry, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Chen Qian
- Department of Chemistry, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Huide Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Peng Yang
- College of Integrated Circuits and Optoelectronic Chips, Shenzhen Technology University, Shenzhen, Guangdong, 518118, China
| | - Zhuomin Zhang
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - You Meng
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Wei Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Zhengbao Yang
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China
| | - Hongyu Yu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Johnny C Ho
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, 999077, China
| | - Chaoliang Tan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, 999077, China
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28
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Liao L, Kovalska E, Regner J, Song Q, Sofer Z. Two-Dimensional Van Der Waals Thin Film and Device. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2303638. [PMID: 37731156 DOI: 10.1002/smll.202303638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/07/2023] [Indexed: 09/22/2023]
Abstract
In the rapidly evolving field of thin-film electronics, the emergence of large-area flexible and wearable devices has been a significant milestone. Although organic semiconductor thin films, which can be manufactured through solution processing, have been identified, their utility is often undermined by their poor stability and low carrier mobility under ambient conditions. However, inorganic nanomaterials can be solution-processed and demonstrate outstanding intrinsic properties and structural stability. In particular, a series of two-dimensional (2D) nanosheet/nanoparticle materials have been shown to form stable colloids in their respective solvents. However, the integration of these 2D nanomaterials into continuous large-area thin with precise control of layer thickness and lattice orientation still remains a significant challenge. This review paper undertakes a detailed analysis of van der Waals thin films, derived from 2D materials, in the advancement of thin-film electronics and optoelectronic devices. The superior intrinsic properties and structural stability of inorganic nanomaterials are highlighted, which can be solution-processed and underscor the importance of solution-based processing, establishing it as a cornerstone strategy for scalable electronic and optoelectronic applications. A comprehensive exploration of the challenges and opportunities associated with the utilization of 2D materials for the next generation of thin-film electronics and optoelectronic devices is presented.
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Affiliation(s)
- Liping Liao
- Department of Inorganic Chemistry, University of Chemistry and Technology, Technicka 5, Prague, 166 28, Czech Republic
| | - Evgeniya Kovalska
- Faculty of Environment, Science and Economy, Department of Engineering, Exeter, EX4 4QF, UK
| | - Jakub Regner
- Department of Inorganic Chemistry, University of Chemistry and Technology, Technicka 5, Prague, 166 28, Czech Republic
| | - Qunliang Song
- School of Materials and Energy, Southwest University, Chongqing, 400715, P. R. China
| | - Zdeněk Sofer
- Department of Inorganic Chemistry, University of Chemistry and Technology, Technicka 5, Prague, 166 28, Czech Republic
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Choi S, Moon T, Wang G, Yang JJ. Filament-free memristors for computing. NANO CONVERGENCE 2023; 10:58. [PMID: 38110639 PMCID: PMC10728429 DOI: 10.1186/s40580-023-00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/06/2023] [Indexed: 12/20/2023]
Abstract
Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.
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Affiliation(s)
- Sanghyeon Choi
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA
| | - Taehwan Moon
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
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Wu G, Zhang X, Feng G, Wang J, Zhou K, Zeng J, Dong D, Zhu F, Yang C, Zhao X, Gong D, Zhang M, Tian B, Duan C, Liu Q, Wang J, Chu J, Liu M. Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing. NATURE MATERIALS 2023; 22:1499-1506. [PMID: 37770677 DOI: 10.1038/s41563-023-01676-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/03/2023] [Indexed: 09/30/2023]
Abstract
Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.
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Affiliation(s)
- Guangjian Wu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
- Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
- Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
| | - Guangdi Feng
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
| | - Jingli Wang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Keji Zhou
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Jinhua Zeng
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Danian Dong
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China
| | - Fangduo Zhu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Chenkai Yang
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
| | - Xiaoming Zhao
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
| | - Danni Gong
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
| | - Mengru Zhang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Bobo Tian
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
| | - Chungang Duan
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
| | - Qi Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
- Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
| | - Jianlu Wang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
- Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
- Institute of Optoelectronics, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Fudan University, Shanghai, China.
| | - Junhao Chu
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- Institute of Optoelectronics, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Fudan University, Shanghai, China
| | - Ming Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China
- Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
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Shibata K, Nishioka D, Namiki W, Tsuchiya T, Higuchi T, Terabe K. Redox-based ion-gating reservoir consisting of (104) oriented LiCoO 2 film, assisted by physical masking. Sci Rep 2023; 13:21060. [PMID: 38030675 PMCID: PMC10687094 DOI: 10.1038/s41598-023-48135-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.
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Affiliation(s)
- Kaoru Shibata
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Department of Applied Physics, Faculty of Science, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Daiki Nishioka
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Department of Applied Physics, Faculty of Science, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Wataru Namiki
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Takashi Tsuchiya
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.
- Department of Applied Physics, Faculty of Science, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
| | - Tohru Higuchi
- Department of Applied Physics, Faculty of Science, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Kazuya Terabe
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
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Li T, Miao J, Fu X, Song B, Cai B, Ge X, Zhou X, Zhou P, Wang X, Jariwala D, Hu W. Reconfigurable, non-volatile neuromorphic photovoltaics. NATURE NANOTECHNOLOGY 2023; 18:1303-1310. [PMID: 37474683 DOI: 10.1038/s41565-023-01446-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/08/2023] [Indexed: 07/22/2023]
Abstract
The neural network image sensor-which mimics neurobiological functions of the human retina-has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrent with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional semiconducting metal sulfides that is concurrently a photovoltaic detector. The device is based on a metal-semiconductor-metal (MSM) two-terminal structure with pulse-tunable sulfur vacancies at the M-S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent can be changed with multiple stable magnitudes. The bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network is finally designed for image processing and object detection using the same device. The results demonstrated that neuromorphic photodetectors can be the key components of visual perception hardware.
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Affiliation(s)
- Tangxin Li
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jinshui Miao
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Xiao Fu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Song
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, China
| | - Bin Cai
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, China
| | - Xun Ge
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Xiaohao Zhou
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Zhou
- School of Microelectronics, Fudan University, Shanghai, China
| | - Xinran Wang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Weida Hu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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Feng C, Wu W, Liu H, Wang J, Wan H, Ma G, Wang H. Emerging Opportunities for 2D Materials in Neuromorphic Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2720. [PMID: 37836361 PMCID: PMC10574516 DOI: 10.3390/nano13192720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023]
Abstract
Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.
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Affiliation(s)
- Chenyin Feng
- Hubei Yangtze Memory Laboratories, Wuhan 430070, China
- Institute of Microelectronics and Integrated Circuits, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Wenwei Wu
- Institute of Microelectronics and Integrated Circuits, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Huidi Liu
- Institute of Microelectronics and Integrated Circuits, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Junke Wang
- Institute of Microelectronics and Integrated Circuits, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Houzhao Wan
- Hubei Yangtze Memory Laboratories, Wuhan 430070, China
| | - Guokun Ma
- Hubei Yangtze Memory Laboratories, Wuhan 430070, China
| | - Hao Wang
- Hubei Yangtze Memory Laboratories, Wuhan 430070, China
- Institute of Microelectronics and Integrated Circuits, School of Microelectronics, Hubei University, Wuhan 430062, China
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Jung G, Kim J, Hong S, Shin H, Jeong Y, Shin W, Kwon D, Choi WY, Lee J. Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302506. [PMID: 37651074 PMCID: PMC10602532 DOI: 10.1002/advs.202302506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/17/2023] [Indexed: 09/01/2023]
Abstract
Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas-sensing materials, and it exhibits low detection limits for H2 S and NH3 gases (0.01 ppm), which are high-protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.
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Affiliation(s)
- Gyuweon Jung
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Jaehyeon Kim
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Seongbin Hong
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Hunhee Shin
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Yujeong Jeong
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Wonjun Shin
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Dongseok Kwon
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Woo Young Choi
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Jong‐Ho Lee
- Department of Electrical and Computer Engineering and Inter‐University Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
- Ministry of Science and ICTSejong30121Republic of Korea
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36
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Pei M, Zhu Y, Liu S, Cui H, Li Y, Yan Y, Li Y, Wan C, Wan Q. Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO 2 Memcapacitive Synapse Arrays. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2305609. [PMID: 37572299 DOI: 10.1002/adma.202305609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/10/2023] [Indexed: 08/14/2023]
Abstract
Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO2 (HZO) for a power-efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium-oxide-based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof-of-concept, a touchless user interface for virtual shopping based on the OMC-based reservoir computing system is demonstrated, benefiting from its interference-robust acoustic and electrophysiological perception. These results shed light on the development of highly power-efficient human-machine interfaces and machine-learning platforms.
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Affiliation(s)
- Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Ying Zhu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Siyao Liu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Hangyuan Cui
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yating Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yang Yan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Changjin Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Qing Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, P. R. China
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Pan X, Shi J, Wang P, Wang S, Pan C, Yu W, Cheng B, Liang SJ, Miao F. Parallel perception of visual motion using light-tunable memory matrix. SCIENCE ADVANCES 2023; 9:eadi4083. [PMID: 37774015 PMCID: PMC10541003 DOI: 10.1126/sciadv.adi4083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Parallel perception of visual motion is of crucial significance to the development of an intelligent machine vision system. However, implementing in-sensor parallel visual motion perception using conventional complementary metal-oxide semiconductor technology is challenging, because the temporal and spatial information embedded in motion cannot be simultaneously encoded and perceived at the sensory level. Here, we demonstrate the parallel perception of diverse motion modes at the sensor level by exploiting light-tunable memory matrix in a van der Waals (vdW) heterostructure array. The optoelectronic characteristics of gate-tunable photoconductivity and light-tunable memory matrix enable devices in the array to realize simultaneous encoding and processing of incoming spatiotemporal light pattern. Furthermore, we implement a visual motion perceptron with the array capable of deciphering multiple motion parameters in parallel, including direction, velocity, acceleration, and angular velocity. Our work opens up a promising venue for the realization of an intelligent machine vision system based on in-sensor motion perception.
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Affiliation(s)
- Xuan Pan
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Jingwen Shi
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Pengfei Wang
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Shuang Wang
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Chen Pan
- Institute of Interdisciplinary Physical Sciences, School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Wentao Yu
- Institute of Interdisciplinary Physical Sciences, School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Bin Cheng
- Institute of Interdisciplinary Physical Sciences, School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Shi-Jun Liang
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Feng Miao
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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Zhang GX, Zhang ZC, Chen XD, Kang L, Li Y, Wang FD, Shi L, Shi K, Liu ZB, Tian JG, Lu TB, Zhang J. Broadband sensory networks with locally stored responsivities for neuromorphic machine vision. SCIENCE ADVANCES 2023; 9:eadi5104. [PMID: 37713483 PMCID: PMC10881039 DOI: 10.1126/sciadv.adi5104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/14/2023] [Indexed: 09/17/2023]
Abstract
As the most promising candidates for the implementation of in-sensor computing, retinomorphic vision sensors can constitute built-in neural networks and directly implement multiply-and-accumulation operations using responsivities as the weights. However, existing retinomorphic vision sensors mainly use a sustained gate bias to maintain the responsivity due to its volatile nature. Here, we propose an ion-induced localized-field strategy to develop retinomorphic vision sensors with nonvolatile tunable responsivity in both positive and negative regimes and construct a broadband and reconfigurable sensory network with locally stored weights to implement in-sensor convolutional processing in spectral range of 400 to 1800 nanometers. In addition to in-sensor computing, this retinomorphic device can implement in-memory computing benefiting from the nonvolatile tunable conductance, and a complete neuromorphic visual system involving front-end in-sensor computing and back-end in-memory computing architectures has been constructed, executing supervised and unsupervised learning tasks as demonstrations. This work paves the way for the development of high-speed and low-power neuromorphic machine vision for time-critical and data-intensive applications.
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Affiliation(s)
- Guo-Xin Zhang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Cheng Zhang
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Xu-Dong Chen
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Lixing Kang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems Division of Advanced Material, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Yuan Li
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Fu-Dong Wang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Lei Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Ke Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Bo Liu
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Jian-Guo Tian
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Tong-Bu Lu
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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40
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Dang B, Liu K, Wu X, Yang Z, Xu L, Yang Y, Huang R. One-Phototransistor-One-Memristor Array with High-Linearity Light-Tunable Weight for Optic Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204844. [PMID: 35917248 DOI: 10.1002/adma.202204844] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.
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Affiliation(s)
- Bingjie Dang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Keqin Liu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Xulei Wu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Zhen Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Liying Xu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yuchao Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, Beijing, 102206, China
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China
| | - Ru Huang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China
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41
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Wan T, Shao B, Ma S, Zhou Y, Li Q, Chai Y. In-Sensor Computing: Materials, Devices, and Integration Technologies. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203830. [PMID: 35808962 DOI: 10.1002/adma.202203830] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/10/2022] [Indexed: 06/15/2023]
Abstract
The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.
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Affiliation(s)
- Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bangjie Shao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Sijie Ma
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yue Zhou
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Qiao Li
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
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Zhou J, Wang Z, Fu Y, Xie Z, Xiao W, Wen Z, Wang Q, Liu Q, Zhang J, He D. A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses. NANOSCALE 2023; 15:13411-13419. [PMID: 37540038 DOI: 10.1039/d3nr01726e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.
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Affiliation(s)
- Jianhong Zhou
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Zheng Wang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Yujun Fu
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Zhichao Xie
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Wei Xiao
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Zhenli Wen
- LONGi Institute of Future Technology Lanzhou University, Lanzhou 730000, China.
| | - Qi Wang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Qiming Liu
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
| | - Junyan Zhang
- Lanzhou Institute of Chemical Physics, Lanzhou 730000, China.
| | - Deyan He
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, China.
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43
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Chen Z, Li W, Fan Z, Dong S, Chen Y, Qin M, Zeng M, Lu X, Zhou G, Gao X, Liu JM. All-ferroelectric implementation of reservoir computing. Nat Commun 2023; 14:3585. [PMID: 37328514 DOI: 10.1038/s41467-023-39371-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/06/2023] [Indexed: 06/18/2023] Open
Abstract
Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO3/SrRuO3 structure via the manipulation of an imprint field (Eimp). It is shown that the volatile FD with Eimp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible Eimp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.
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Affiliation(s)
- Zhiwei Chen
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Wenjie Li
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Zhen Fan
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
| | - Shuai Dong
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Yihong Chen
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Minghui Qin
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Min Zeng
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Xubing Lu
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Guofu Zhou
- National Center for International Research on Green Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Xingsen Gao
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
| | - Jun-Ming Liu
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China
- Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
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Sun Y, Lin T, Lei N, Chen X, Kang W, Zhao Z, Wei D, Chen C, Pang S, Hu L, Yang L, Dong E, Zhao L, Liu L, Yuan Z, Ullrich A, Back CH, Zhang J, Pan D, Zhao J, Feng M, Fert A, Zhao W. Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system. Nat Commun 2023; 14:3434. [PMID: 37301906 DOI: 10.1038/s41467-023-39207-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg1/3Nb2/3O3-0.3PbTiO3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications.
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Affiliation(s)
- Yiming Sun
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Tao Lin
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Na Lei
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China.
| | - Xing Chen
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Wang Kang
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Zhiyuan Zhao
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
| | - Dahai Wei
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
| | - Chao Chen
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Simin Pang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Linglong Hu
- Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education, Jilin Normal University, Changchun, 130103, China
| | - Liu Yang
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Enxuan Dong
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
| | - Li Zhao
- The Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, 100875, China
| | - Lei Liu
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
| | - Zhe Yuan
- The Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, 100875, China
| | - Aladin Ullrich
- Institute of Physics, University of Augsburg, Augsburg, 86159, Germany
| | - Christian H Back
- Department of Physics, Technical University of Munich, Garching, 85748, Germany
- Munich Center for Quantum Science and Technology (MCQST), Munich, 80799, Germany
- Centre for Quantum Engineering (ZQE), Technical University of Munich, 85748, Garching, Germany
| | - Jun Zhang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center of Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dong Pan
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
| | - Jianhua Zhao
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
| | - Ming Feng
- Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education, Jilin Normal University, Changchun, 130103, China.
| | - Albert Fert
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, Palaiseau, 91767, France
| | - Weisheng Zhao
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
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45
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Cao Z, Sun B, Zhou G, Mao S, Zhu S, Zhang J, Ke C, Zhao Y, Shao J. Memristor-based neural networks: a bridge from device to artificial intelligence. NANOSCALE HORIZONS 2023; 8:716-745. [PMID: 36946082 DOI: 10.1039/d2nh00536k] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.
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Affiliation(s)
- Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi'an 710123, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, China
| | - Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jie Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Chuan Ke
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
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46
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Dong X, Wei W, Sun H, Li S, Chen J, Chen J, Zhang X, Zhao Y, Li Y. Neotype kuramite optoelectronic memristor for bio-synaptic plasticity simulations. J Chem Phys 2023; 158:2889009. [PMID: 37154283 DOI: 10.1063/5.0151205] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 04/12/2023] [Indexed: 05/10/2023] Open
Abstract
Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼-0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and "learning-forgetting-learning" behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions.
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Affiliation(s)
- Xiaofei Dong
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Wenbin Wei
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Hao Sun
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Siyuan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
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47
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Liu X, Sun C, Guo Z, Xia X, Jiang Q, Ye X, Shang J, Zhang Y, Zhu X, Li RW. Near-Sensor Reservoir Computing for Gait Recognition via a Multi-Gate Electrolyte-Gated Transistor. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300471. [PMID: 36950731 DOI: 10.1002/advs.202300471] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/23/2023] [Indexed: 05/27/2023]
Abstract
The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Zhecheng Guo
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Xiangling Xia
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Shang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Yuejun Zhang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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48
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Zha J, Shi S, Chaturvedi A, Huang H, Yang P, Yao Y, Li S, Xia Y, Zhang Z, Wang W, Wang H, Wang S, Yuan Z, Yang Z, He Q, Tai H, Teo EHT, Yu H, Ho JC, Wang Z, Zhang H, Tan C. Electronic/Optoelectronic Memory Device Enabled by Tellurium-based 2D van der Waals Heterostructure for in-Sensor Reservoir Computing at the Optical Communication Band. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211598. [PMID: 36857506 DOI: 10.1002/adma.202211598] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/16/2023] [Indexed: 05/19/2023]
Abstract
Although 2D materials are widely explored for data storage and neuromorphic computing, the construction of 2D material-based memory devices with optoelectronic responsivity in the short-wave infrared (SWIR) region for in-sensor reservoir computing (RC) at the optical communication band still remains a big challenge. In this work, an electronic/optoelectronic memory device enabled by tellurium-based 2D van der Waals (vdW) heterostructure is reported, where the ferroelectric CuInP2 S6 and tellurium channel endow this device with both the long-term potentiation/depression by voltage pulses and short-term potentiation by 1550 nm laser pulses (a typical wavelength in the conventional fiber optical communication band). Leveraging the rich dynamics, a fully memristive in-sensor RC system that can simultaneously sense, decode, and learn messages transmitted by optical fibers is demonstrated. The reported 2D vdW heterostructure-based memory featuring both the long-term and short-term memory behaviors using electrical and optical pulses in SWIR region has not only complemented the wide spectrum of applications of 2D materials family in electronics/optoelectronics but also paves the way for future smart signal processing systems at the edge.
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Affiliation(s)
- Jiajia Zha
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Shuhui Shi
- Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, 999077, P. R. China
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China
| | - Apoorva Chaturvedi
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore
| | - Haoxin Huang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Peng Yang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Yao Yao
- Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Siyuan Li
- Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Yunpeng Xia
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Zhuomin Zhang
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Wei Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Huide Wang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Shaocong Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Zhen Yuan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 610054, P. R. China
| | - Zhengbao Yang
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Department of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong, 999077, P. R. China
| | - Qiyuan He
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Huiling Tai
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 610054, P. R. China
| | - Edwin Hang Tong Teo
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore
- School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Hongyu Yu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China
| | - Johnny C Ho
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Hua Zhang
- Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Hong Kong Branch of National Precious Metals Material Engineering Research Center (NPMM), City University of Hong Kong, Hong Kong, P. R. China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, 518057, P. R. China
| | - Chaoliang Tan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, P. R. China
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, 518057, P. R. China
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49
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Tan H, van Dijken S. Dynamic machine vision with retinomorphic photomemristor-reservoir computing. Nat Commun 2023; 14:2169. [PMID: 37061543 PMCID: PMC10105772 DOI: 10.1038/s41467-023-37886-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 03/30/2023] [Indexed: 04/17/2023] Open
Abstract
Dynamic machine vision requires recognizing the past and predicting the future of a moving object based on present vision. Current machine vision systems accomplish this by processing numerous image frames or using complex algorithms. Here, we report motion recognition and prediction in recurrent photomemristor networks. In our system, a retinomorphic photomemristor array, working as dynamic vision reservoir, embeds past motion frames as hidden states into the present frame through inherent dynamic memory. The informative present frame facilitates accurate recognition of past and prediction of future motions with machine learning algorithms. This in-sensor motion processing capability eliminates redundant data flows and promotes real-time perception of moving objects for dynamic machine vision.
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Affiliation(s)
- Hongwei Tan
- NanoSpin, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, FI-00076, Aalto, Finland.
| | - Sebastiaan van Dijken
- NanoSpin, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, FI-00076, Aalto, Finland.
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50
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Eshete YA, Hwang E, Kim J, Nguyen PL, Yu WJ, Kong BS, Jang MS, Lee J, Cho S, Yang H. A Polymorphic Memtransistor with Tunable Metallic and Semiconducting Channel. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209089. [PMID: 36655805 DOI: 10.1002/adma.202209089] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 01/15/2023] [Indexed: 06/17/2023]
Abstract
Modulating semiconducting channel potential has been used for electrical switching in transistors without biological plasticity operations that are critical for energy-efficient neuromorphic computing. To achieve efficient data processing, alternative transport mechanisms, such as tunneling and thermionic emission, have been introduced with 2D materials. Here, a polymorphic memtransistor based on atomically thin Mo0.91 W0.09 Te2 is presented, where the lattice and electronic structures of the lateral device channel can be tuned as either metallic (1T') or semiconducting (2H) phases by electrical gating. The structural and electronic phase change of the channel material, optimized in Mo0.91 W0.09 Te2 , is explored using transport and optical measurements at the device scale. Based on the phase transition, the polymorphic memtransistor demonstrates a high on/off ratio (up to 105 ), low subthreshold swing (down to 80 mV dec-1 ), and various memristive behaviors, which are distinguished from traditional phase-change memory, transistors, and passive memristors for diverse neuromorphic and in-memory computing.
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Affiliation(s)
- Yonas Assefa Eshete
- Department of Energy Science, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Eunji Hwang
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Junhyung Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Phuong Lien Nguyen
- Department of Physics, Pusan National University, Busan, 46241, South Korea
| | - Woo Jong Yu
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Bai Sun Kong
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Min Seok Jang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Jaekwang Lee
- Department of Physics, Pusan National University, Busan, 46241, South Korea
| | - Suyeon Cho
- Division of Chemical Engineering and Materials Science, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, South Korea
| | - Heejun Yang
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
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