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Li W, Li J, Mu T, Li J, Sun P, Dai M, Chen Y, Yang R, Chen Z, Wang Y, Wu Y, Wang S. The Nonvolatile Memory and Neuromorphic Simulation of ReS 2/h-BN/Graphene Floating Gate Devices Under Photoelectrical Hybrid Modulations. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2311630. [PMID: 38470212 DOI: 10.1002/smll.202311630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/02/2024] [Indexed: 03/13/2024]
Abstract
The floating gate devices, as a kind of nonvolatile memory, obtain great application potential in logic-in-memory chips. The 2D materials have been greatly studied due to atomically flat surfaces, higher carrier mobility, and excellent photoelectrical response. The 2D ReS2 flake is an excellent candidate for channel materials due to thickness-independent direct bandgap and outstanding optoelectronic response. In this paper, the floating gate devices are prepared with the ReS2/h-BN/Gr heterojunction. It obtains superior nonvolatile electrical memory characteristics, including a higher memory window ratio (81.82%), tiny writing/erasing voltage (±8 V/2 ms), long retention (>1000 s), and stable endurance (>1000 times) as well as multiple memory states. Meanwhile, electrical writing and optical erasing are achieved by applying electrical and optical pulses, and multilevel storage can easily be achieved by regulating light pulse parameters. Finally, due to the ideal long-time potentiation/depression synaptic weights regulated by light pulses and electrical pulses, the convolutional neural network (CNN) constructed by ReS2/h-BN/Gr floating gate devices can achieve image recognition with an accuracy of up to 98.15% for MNIST dataset and 91.24% for Fashion-MNIST dataset. The research work adds a powerful option for 2D materials floating gate devices to apply to logic-in-memory chips and neuromorphic computing.
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Affiliation(s)
- Wei Li
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Jiaying Li
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Tianhui Mu
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Jiayao Li
- School of Statistics, Wuhan University of Science and Technology, 947 Heping Avenue, Qingshan District, Wuhan, Hubei, 430081, P. R. China
| | - Pengcheng Sun
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Mingjian Dai
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yuhua Chen
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Ruijing Yang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Zhao Chen
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yucheng Wang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yupan Wu
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Shaoxi Wang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
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2
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Zhao X, Zou H, Wang M, Wang J, Wang T, Wang L, Chen X. Conformal Neuromorphic Bioelectronics for Sense Digitalization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403444. [PMID: 38934554 DOI: 10.1002/adma.202403444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/03/2024] [Indexed: 06/28/2024]
Abstract
Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.
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Affiliation(s)
- Xiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Haochen Zou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai, 200433, China
| | - Jianwu Wang
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lianhui Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaodong Chen
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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3
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Fang H, Wang J, Nie F, Zhang N, Yu T, Zhao L, Shi C, Zhang P, He B, Lü W, Zheng L. Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:1015-1024. [PMID: 38156871 DOI: 10.1021/acsami.3c13171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.
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Affiliation(s)
- Hong Fang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Jie Wang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Fang Nie
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Nana Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Tongliang Yu
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Le Zhao
- School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Chaoqun Shi
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Peng Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Bin He
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Weiming Lü
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Limei Zheng
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
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Guo Z, Liu G, Sun Y, Zhang Y, Zhao J, Liu P, Wang H, Zhou Z, Zhao Z, Jia X, Sun J, Shao Y, Han X, Zhang Z, Yan X. High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO 3:MgO Film Memristor. ACS NANO 2023; 17:21518-21530. [PMID: 37897737 DOI: 10.1021/acsnano.3c06510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
Neuromorphic computing based on memristors capable of in-memory computing is promising to break the energy and efficiency bottleneck of well-known von Neumann architectures. However, unstable and nonlinear conductance updates compromise the recognition accuracy and block the integration of neural network hardware. To this end, we present a highly stable memristor with self-assembled vertically aligned nanocomposite (VAN) SrTiO3:MgO films that achieve excellent resistive switching with low set/reset voltage variability (4.7%/-5.6%) and highly linear conductivity variation (nonlinearity = 0.34) by spatially limiting the conductive channels at the vertical interfaces. Various synaptic behaviors are simulated by continuously modulating the conductance. Especially, convolutional image processing using diverse crossbar kernels is demonstrated, and the artificial neural network achieves an overwhelming recognition accuracy of up to 97.50% for handwritten digits. Even under the perturbation of Poisson noise (λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%, respectively. Importantly, the logic memory function is proven experimentally based on the nonvolatile properties. This work provides a material system and design idea to achieve high-performance neuromorphic computing and logic operation.
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Affiliation(s)
- Zhenqiang Guo
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Gongjie Liu
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Yong Sun
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Yinxing Zhang
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Jianhui Zhao
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Pan Liu
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Hong Wang
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Zhenyu Zhou
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Zhen Zhao
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Xiaotong Jia
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Jiameng Sun
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Yiduo Shao
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Xu Han
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Zixuan Zhang
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Xiaobing Yan
- Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China
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5
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Park E, Jang S, Noh G, Jo Y, Lee DK, Kim IS, Song HC, Kim S, Kwak JY. Indium-Gallium-Zinc Oxide-Based Synaptic Charge Trap Flash for Spiking Neural Network-Restricted Boltzmann Machine. NANO LETTERS 2023; 23:9626-9633. [PMID: 37819875 DOI: 10.1021/acs.nanolett.3c03510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.
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Affiliation(s)
- Eunpyo Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
- Department of Materials Science & Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Suyeon Jang
- Department of Materials Science & Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Research Institute of Advanced Materials (RIAM), Seoul National University, Seoul 08826, Republic of Korea
- Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul 08826, Republic of Korea
| | - Gichang Noh
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Yooyeon Jo
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Dae Kyu Lee
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - In Soo Kim
- Nanophotonics Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
- KIST-SKKU Carbon-Neutral Research Center, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
| | - Hyun-Cheol Song
- KIST-SKKU Carbon-Neutral Research Center, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Sangbum Kim
- Department of Materials Science & Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Research Institute of Advanced Materials (RIAM), Seoul National University, Seoul 08826, Republic of Korea
- Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul 08826, Republic of Korea
| | - Joon Young Kwak
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
- Division of Nanoscience and Technology, Korea University of Science and Technology (UST), Daejeon 34113, Republic of Korea
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6
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Won UY, An Vu Q, Park SB, Park MH, Dam Do V, Park HJ, Yang H, Lee YH, Yu WJ. Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning. Nat Commun 2023; 14:3070. [PMID: 37244897 DOI: 10.1038/s41467-023-38667-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 05/10/2023] [Indexed: 05/29/2023] Open
Abstract
Multi-terminal memristor and memtransistor (MT-MEMs) has successfully performed complex functions of heterosynaptic plasticity in synapse. However, theses MT-MEMs lack the ability to emulate membrane potential of neuron in multiple neuronal connections. Here, we demonstrate multi-neuron connection using a multi-terminal floating-gate memristor (MT-FGMEM). The variable Fermi level (EF) in graphene allows charging and discharging of MT-FGMEM using horizontally distant multiple electrodes. Our MT-FGMEM demonstrates high on/off ratio over 105 at 1000 s retention about ~10,000 times higher than other MT-MEMs. The linear behavior between current (ID) and floating gate potential (VFG) in triode region of MT-FGMEM allows for accurate spike integration at the neuron membrane. The MT-FGMEM fully mimics the temporal and spatial summation of multi-neuron connections based on leaky-integrate-and-fire (LIF) functionality. Our artificial neuron (150 pJ) significantly reduces the energy consumption by 100,000 times compared to conventional neurons based on silicon integrated circuits (11.7 μJ). By integrating neurons and synapses using MT-FGMEMs, a spiking neurosynaptic training and classification of directional lines functioned in visual area one (V1) is successfully emulated based on neuron's LIF and synapse's spike-timing-dependent plasticity (STDP) functions. Simulation of unsupervised learning based on our artificial neuron and synapse achieves a learning accuracy of 83.08% on the unlabeled MNIST handwritten dataset.
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Affiliation(s)
- Ui Yeon Won
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Hyundai motors group, Electronic Devices research Team, Uiwang, 16082, South Korea
| | - Quoc An Vu
- IBS Center for Integrated Nanostructure Physics, Institute for Basic Science, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Sung Bum Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Mi Hyang Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Van Dam Do
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Hyun Jun Park
- Display R&D Group, Mobile Communication Business, Samsung Electronics, Suwon, 16677, South Korea
| | - Heejun Yang
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Young Hee Lee
- IBS Center for Integrated Nanostructure Physics, Institute for Basic Science, Sungkyunkwan University, Suwon, 16419, South Korea.
- Department of Energy Science, Sungkyunkwan University, Suwon, 16419, South Korea.
| | - Woo Jong Yu
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.
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Zhang F, Li C, Li Z, Dong L, Zhao J. Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. MICROSYSTEMS & NANOENGINEERING 2023; 9:16. [PMID: 36817330 PMCID: PMC9935897 DOI: 10.1038/s41378-023-00487-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.
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Affiliation(s)
- Fanqing Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Chunyang Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Zhongyi Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong, China
| | - Jing Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
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Hu M, Yu J, Chen Y, Wang S, Dong B, Wang H, He Y, Ma Y, Zhuge F, Zhai T. A non-linear two-dimensional float gate transistor as a lateral inhibitory synapse for retinal early visual processing. MATERIALS HORIZONS 2022; 9:2335-2344. [PMID: 35820170 DOI: 10.1039/d2mh00466f] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Synaptic transistors that accommodate concurrent signal transmission and learning in a neural network are attracting enormous interest for neuromorphic sensory processing. To remove redundant sensory information while keeping important features, artificial synaptic transistors with non-linear conductance are desired to apply filter processing to sensory inputs. Here, we report the realization of non-linear synapses using a two-dimensional van der Waals (vdW) heterostructure (MoS2/h-BN/graphene) based float gate memory device, in which the semiconductor channel is tailored via a surface acceptor (ZnPc) for subthreshold operation. In addition to usual synaptic plasticity, the memory device exhibits highly non-linear conductance (rectification ratio >106), allowing bidirectional yet only negative/inhibitory current to pass through. We demonstrate that in a lateral coupling network, such a float gate memory device resembles the key lateral inhibition function of horizontal cells for the formation of an ON-center/OFF-surround receptive field. When combined with synaptic plasticity, the lateral inhibition weights are further tunable to enable adjustable edge enhancement for early visual processing. Our results here hopefully open a new scheme toward early sensory perception via lateral inhibitory synaptic transistors.
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Affiliation(s)
- Man Hu
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
| | - Jun Yu
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
| | - Yangyang Chen
- School of optoelectronic and information, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China
| | - Siqi Wang
- School of optoelectronic and information, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China
| | - Boyi Dong
- School of optoelectronic and information, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China
| | - Han Wang
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
| | - Yuhui He
- School of optoelectronic and information, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China
| | - Ying Ma
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
| | - Fuwei Zhuge
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
| | - Tianyou Zhai
- State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, P. R. China.
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9
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Kim TS, Dhakal KP, Park E, Noh G, Chai HJ, Kim Y, Oh S, Kang M, Park J, Kim J, Kim S, Jeong HY, Bang S, Kwak JY, Kim J, Kang K. Gas-Phase Alkali Metal-Assisted MOCVD Growth of 2D Transition Metal Dichalcogenides for Large-Scale Precise Nucleation Control. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2106368. [PMID: 35451163 DOI: 10.1002/smll.202106368] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/06/2022] [Indexed: 06/14/2023]
Abstract
Advances in large-area and high-quality 2D transition metal dichalcogenides (TMDCs) growth are essential for semiconductor applications. Here, the gas-phase alkali metal-assisted metal-organic chemical vapor deposition (GAA-MOCVD) of 2D TMDCs is reported. It is determined that sodium propionate (SP) is an ideal gas-phase alkali-metal additive for nucleation control in the MOCVD of 2D TMDCs. The grain size of MoS2 in the GAA-MOCVD process is larger than that in the conventional MOCVD process. This method can be applied to the growth of various TMDCs (MoS2 , MoSe2 , WSe2 , and WSe2 ) and the generation of large-scale continuous films. Furthermore, the growth behaviors of MoS2 under different SP and oxygen injection time conditions are systematically investigated to determine the effects of SP and oxygen on nucleation control in the GAA-MOCVD process. It is found that the combination of SP and oxygen increases the grain size and nucleation suppression of MoS2 . Thus, the GAA-MOCVD with a precise and controllable supply of a gas-phase alkali metal and oxygen allows achievement of optimum growth conditions that maximizes the grain size of MoS2 . It is expected that GAA-MOCVD can provide a way for batch fabrication of large-scale atomically thin electronic devices based on 2D semiconductors.
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Affiliation(s)
- Tae Soo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Krishna P Dhakal
- Department of Energy Science, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Eunpyo Park
- Center for Neuromorphic Engineering, Korea Institute Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Gichang Noh
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Hyun-Jun Chai
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Youngbum Kim
- Department of Energy Science, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Saeyoung Oh
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Minsoo Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jeongwon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jaewoo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Suhyun Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hu Young Jeong
- UNIST Central Research Facilities (UCRF) and Departmet of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Sunghwan Bang
- Materials & Production Engineering Research Institute, LG Electronics, Pyeongtaek-si, 17709, Republic of Korea
| | - Joon Young Kwak
- Center for Neuromorphic Engineering, Korea Institute Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Jeongyong Kim
- Department of Energy Science, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Kibum Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
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Wang Y, Huang W, Zhang Z, Fan L, Huang Q, Wang J, Zhang Y, Zhang M. Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory. NANOSCALE 2021; 13:11360-11369. [PMID: 34096562 DOI: 10.1039/d1nr02099d] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human-machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent applications.
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Affiliation(s)
- Yarong Wang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Weihong Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Ziwei Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Lingchong Fan
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Qiuyue Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Jiaxin Wang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Yiming Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Min Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
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