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Zhao W, Lin Z, Chen H, Xu S, Chen E, Guo T, Ye Y, Chen H. Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition. NANO LETTERS 2024; 24:9937-9945. [PMID: 39092599 DOI: 10.1021/acs.nanolett.4c02447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The processing of multicolor noisy images in visual neuromorphic devices requires selective absorption at specific wavelengths; however, it is difficult to achieve this because the spectral absorption range of the device is affected by the type of material. Surprisingly, the absorption range of perovskite materials can be adjusted by doping. Herein, a CdCl2 co-doped CsPbBr3 nanocrystal-based photosensitive synaptic transistor (PST) is reported. By decreasing the doping concentration, the response of the PST to short-wavelength light is gradually enhanced, and even weak light of 40 μW·cm-2 can be detected. Benefiting from the excellent color selectivity of the PST device, the device array is applied to feature extraction of target blue items and removal of red and green noise, which results in the recognition accuracy of 95% for the noisy MNIST data set. This work provides new ideas for the application of novel transistors integrating sensors and storage computing.
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Affiliation(s)
- Wenxiao Zhao
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Zexi Lin
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Hao Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Sheng Xu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Enguo Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Yun Ye
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
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Khan R, Rahman NU, Hayat MF, Ghernaout D, Salih AAM, Ashraf GA, Samad A, Mahmood MA, Rahman N, Sohail M, Iqbal S, Abdullaev S, Khan A. Unveiling cutting-edge developments: architectures and nanostructured materials for application in optoelectronic artificial synapses. NANOSCALE 2024; 16:14589-14620. [PMID: 39011743 DOI: 10.1039/d4nr00904e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
One possible result of low-level characteristics in the traditional von Neumann formulation system is brain-inspired photonics technology based on human brain idea. Optoelectronic neural devices, which are accustomed to imitating the sensory role of biological synapses by adjusting connection measures, can be used to fabricate highly reliable neurologically calculating devices. In this case, nanosized materials and device designs are attracting attention since they provide numerous potential benefits in terms of limited cool contact, rapid transfer fluidity, and the capture of photocarriers. In addition, the combination of classic nanosized photodetectors with recently generated digital synapses offers promising results in a variety of practical applications, such as data processing and computation. Herein, we present the progress in constructing improved optoelectronic synaptic devices that rely on nanomaterials, for example, 0-dimensional (quantum dots), 1-dimensional, and 2-dimensional composites, besides the continuously developing mixed heterostructures. Furthermore, the challenges and potential prospects linked with this field of study are discussed in this paper.
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Affiliation(s)
- Rajwali Khan
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Naveed Ur Rahman
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Djamel Ghernaout
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Chemical Engineering Department, Faculty of Engineering, University of Blida, PO Box 270, Blida 09000, Algeria
| | - Alsamani A M Salih
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Department of Chemical Engineering, Faculty of Engineering, Al Neelain University, Khartoum 12702, Sudan
| | | | - Abdus Samad
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Nasir Rahman
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Mohammad Sohail
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Shahid Iqbal
- Department of Physics, University of Wisconsin, La Crosse, WI 54601, USA
| | - Sherzod Abdullaev
- Senior Researcher, Engineering School, Central Asian University, Tashkent, Uzbekistan
- Senior Researcher, Scientific and Innovation Department, Tashkent State Pedagogical University, Uzbekistan
| | - Alamzeb Khan
- Yale University School of Medicine, New Haven, Connecticut, USA
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Zhou Z, Wu Y, Pan K, Zhu D, Li Z, Yan S, Xin Q, Wang Q, Qian X, Xiu F, Huang W, Liu J. A memristive-photoconductive transduction methodology for accurately nondestructive memory readout. LIGHT, SCIENCE & APPLICATIONS 2024; 13:175. [PMID: 39043644 PMCID: PMC11266504 DOI: 10.1038/s41377-024-01519-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 06/11/2024] [Accepted: 07/01/2024] [Indexed: 07/25/2024]
Abstract
Crossbar resistive memory architectures enable high-capacity storage and neuromorphic computing, accurate retrieval of the stored information is a prerequisite during read operation. However, conventional electrical readout normally suffer from complicated process, inaccurate and destructive reading due to crosstalk effect from sneak path current. Here we report a memristive-photoconductive transduction (MPT) methodology for precise and nondestructive readout in a memristive crossbar array. The individual devices present dynamic filament form/fuse for resistance modulation under electric stimulation, which leads to photogenerated carrier transport for tunable photoconductive response under subsequently light pulse stimuli. This coherent signal transduction can be used to directly detect the memorized on/off states stored in each cell, and a prototype 4 * 4 crossbar memories has been constructed and validated for the fidelity of crosstalk-free readout in recall process.
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Affiliation(s)
- Zhe Zhou
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Yueyue Wu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Duoyi Zhu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Zifan Li
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Shiqi Yan
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Qian Xin
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Qiye Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Xinkai Qian
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Fei Xiu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
| | - Wei Huang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, 710072, China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China.
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Tan D, Zhang Z, Shi H, Sun N, Li Q, Bi S, Huang J, Liu Y, Guo Q, Jiang C. Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2407751. [PMID: 39011791 DOI: 10.1002/adma.202407751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/27/2024] [Indexed: 07/17/2024]
Abstract
In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.
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Affiliation(s)
- Dongchen Tan
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Zhaorui Zhang
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Haohao Shi
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Nan Sun
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Qikun Li
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, China
| | - Sheng Bi
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Jijie Huang
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Yiheng Liu
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
| | - Qinglei Guo
- Department of Material Science and Engineering, Frederick Seitz Material Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Chengming Jiang
- State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China
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5
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Pan Y, Wang Q, He A, Yan Y, Cao X, Liu P, Jiang Y. Effect of annealing temperature on the optoelectrical synapse behaviors of A-ZnO microtube. DISCOVER NANO 2024; 19:116. [PMID: 39002101 PMCID: PMC11246399 DOI: 10.1186/s11671-024-04060-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/25/2024] [Indexed: 07/15/2024]
Abstract
Optoelectronic synapses with fast response, low power consumption, and memory function hold great potential in the future of artificial intelligence technologies. Herein, a strategy of annealing in oxygen ambient at different temperatures is presented to improve the optoelectronic synaptic behaviors of acceptor-rich ZnO (A-ZnO) microtubes. The basic synaptic functions of as-grown and annealed A-ZnO microtubes including excitatory postsynaptic current (EPSC), short-term memory (STM) to long-term memory (LTM) conversion, and paired-pulse facilitation (PPF), were successfully emulated. The results show that the annealing temperature of 600 °C yields high figures of merit compared to other annealed A-ZnO microtubes. The 4-fold and 20-fold enhancement dependent on the light pulse duration time and energy density have been achieved in the 600 °C annealed A-ZnO microtube, respectively. Furthermore, the device exhibited a PPF index of up to 238% and achieved four cycles of "learning-forgetting" process, proving its capability for optical information storage. The free exciton (FX) and donor-acceptor pair (DAP) concentrations significantly influenced the persistent photoconductivity (PPC) behavior of A-ZnO microtubes. Therefore, the LTM response can be controlled by the adjustment of numbers, powers, and interval time of the optical stimulation. This work outlines a strategy to improve the EPSC response through defect control, representing a step towards applications in the field of optoelectronic synaptic device.
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Affiliation(s)
- Yongman Pan
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Qiang Wang
- College of New Materials and Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Anqing He
- College of Materials Science and Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Yinzhou Yan
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Xingzhong Cao
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Peng Liu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Yijian Jiang
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China.
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6
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Leng K, Wan Y, Fu Y, Wang L, Wang Q. Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309945. [PMID: 38400705 DOI: 10.1002/smll.202309945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Indexed: 02/25/2024]
Abstract
In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.
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Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yu Wan
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yao Fu
- Department of Materials, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Li Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Qisheng Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
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Guo F, Liu Y, Zhang M, Yu W, Li S, Zhang B, Hu B, Zhong L, Jie W, Hao L. A Dual-Functional Integration of Photodetectors and Artificial Optoelectronic Synapses on a VO 2/WO 3 Heterojunction Device. SMALL METHODS 2024:e2400779. [PMID: 38940078 DOI: 10.1002/smtd.202400779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/18/2024] [Indexed: 06/29/2024]
Abstract
Bionic visual systems require multimodal integration of eye-like photodetectors and brain-like image memory. However, the integration of photodetectors (PDs) and artificial optoelectronic synapses devices (OESDs) by one device remains a giant challenge due to their photoresponse discrepancy. Herein, a dual-functional integration of PDs and OESDs based on VO2/WO3 heterojunctions is presented. The device can be able to realize a dual-mode conversion between PDs and OESDs through tuning the bias voltage. Under zero bias voltage, the device exhibiting excellent photodetecting behaviors based on the photovoltaic effect, showing a high self-powered photoresponsivity of 18.5 mA W-1 and high detectivity of 7.5 × 1010 Jones with fast photoresponse. When the external bias voltages are applied, it can be acted as an OESD and exhibit versatile electrical and photonic synaptic characteristics based on the trapping and detrapping effects, including synaptic plasticity and learning-experience behaviors. More importantly, benefiting from the excellent photosensing ability and transporting properties, the device shows ultralow-power consumption of 39.0 pJ and a 4 × 4 OESDs array is developed to realize the visual perception and memory. This work not only supplies a novel route to realize complex functional integration just in one device, but also offers effective strategies for developing neuromorphic visual system.
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Affiliation(s)
- Fuhai Guo
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Yunjie Liu
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Mingcong Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Weizhuo Yu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Siqi Li
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bo Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bing Hu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Lun Zhong
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China
| | - Wenjing Jie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China
| | - Lanzhong Hao
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
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8
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Meng Y, Cheng G. Human somatosensory systems based on sensor-memory-integrated technology. NANOSCALE 2024; 16:11928-11958. [PMID: 38847091 DOI: 10.1039/d3nr06521a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.
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Affiliation(s)
- Yanfang Meng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
| | - Guanggui Cheng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
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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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10
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Lai XC, Tang Z, Fang J, Feng L, Yao DJ, Zhang L, Jiang YP, Liu QX, Tang XG, Zhou YC, Shang J, Zhong GK, Gao J. An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing. MATERIALS HORIZONS 2024; 11:2886-2897. [PMID: 38563639 DOI: 10.1039/d4mh00064a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.
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Affiliation(s)
- Xi-Cai Lai
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Zhenhua Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Junlin Fang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Leyan Feng
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Di-Jie Yao
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Li Zhang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yan-Ping Jiang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Qiu-Xiang Liu
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Xin-Gui Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yi-Chun Zhou
- School of Advanced Materials and Nanotechnology, Xidian University, Xian 710126, China
| | - Jie Shang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Gao-Kuo Zhong
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ju Gao
- Department of Physics, The University of Hong Kong, Hong Kong 999077, P. R. China
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11
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Lee M, Kim Y, Mo SH, Kim S, Eom K, Lee H. Optoelectronic Synapse Based on 2D Electron Gas in Stoichiometry-Controlled Oxide Heterostructures. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309851. [PMID: 38214690 DOI: 10.1002/smll.202309851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/17/2023] [Indexed: 01/13/2024]
Abstract
Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5 × 5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.
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Affiliation(s)
- Minkyung Lee
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
| | - Youngmin Kim
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
| | - Sang Hyeon Mo
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Kitae Eom
- Department of Electronic Engineering, Gachon University, Seongnam, 13120, Republic of Korea
| | - Hyungwoo Lee
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
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12
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Li X, Fang Z, Guo X, Wang R, Zhao Y, Zhu W, Wang L, Zhang L. Light-Induced Conductance Potentiation and Depression in an All-Optically Controlled Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:27866-27874. [PMID: 38747412 DOI: 10.1021/acsami.4c02092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Optoelectronic memristors are new multifunctional devices with both electrically tunable and light-tunable synaptic plasticity, attracting great attention as key promising devices for optoelectronic neuromorphic computing systems. At present, the conductance modulation in most optoelectronic memristors is conducted in a hybrid photoelectric mode, suffering some problems such as heat generation and control complexity. Here, an optoelectronic memristor based on the p+-Si/n-ZnO heterojunction is proposed where the conductance can be reversibly modulated in an all-optically controlled mode. The electron detrapping/trapping mechanism at the p+-Si/n-ZnO interface barrier region is presented to explain the light-induced conductance potentiation/depression behavior. Furthermore, some synaptic functions, including excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), and paired-pulse facilitation (PPF), are successfully mimicked in the p+-Si/n-ZnO heterojunction memristor, instructing its application potential for optoelectronic neuromorphic computing.
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Affiliation(s)
- Xinmiao Li
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Zijing Fang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Xing Guo
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Ruixiao Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Yinxi Zhao
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Wenhui Zhu
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Liancheng Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Lei Zhang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
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13
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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.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: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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14
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Deng Y, Liu S, Ma X, Guo S, Zhai B, Zhang Z, Li M, Yu Y, Hu W, Yang H, Kapitonov Y, Han J, Wu J, Li Y, Zhai T. Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309940. [PMID: 38373410 DOI: 10.1002/adma.202309940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/28/2024] [Indexed: 02/21/2024]
Abstract
The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.
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Affiliation(s)
- Yao Deng
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Shenghong Liu
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Xiaoxi Ma
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Shuyang Guo
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Baoxing Zhai
- Institute of Semiconductors, Henan Academy of Sciences, Zhengzhou, 450046, P. R. China
| | - Zihan Zhang
- Department of Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Manshi Li
- Wuhan National High Magnetic Field Centre, Department of Physics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yimeng Yu
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Nanostructure Research Center, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Wenhua Hu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Hui Yang
- Department of Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yury Kapitonov
- Department of Photonics, Saint Petersburg State University, Saint Petersburg, 199034, Russia
| | - Junbo Han
- Wuhan National High Magnetic Field Centre, Department of Physics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jinsong Wu
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Nanostructure Research Center, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Yuan Li
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
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15
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Liu S, Wu Z, He Z, Chen W, Zhong X, Guo B, Liu S, Duan H, Guo Y, Zeng J, Liu G. Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception. ACS APPLIED MATERIALS & INTERFACES 2024; 16:22303-22311. [PMID: 38626428 DOI: 10.1021/acsami.4c04398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.
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Affiliation(s)
- Sixian Liu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Zhixin Wu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Zhilong He
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Weilin Chen
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Xiaolong Zhong
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Bingjie Guo
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Shuzhi Liu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Hongxiao Duan
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Yanbo Guo
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Jianmin Zeng
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Gang Liu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
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16
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Tan D, Sun N, Huang J, Zhang Z, Zeng L, Li Q, Bi S, Bu J, Peng Y, Guo Q, Jiang C. Monolayer Vacancy-Induced MXene Memory for Write-Verify-Free Programming. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402273. [PMID: 38682587 DOI: 10.1002/smll.202402273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The fundamental logic states of 1 and 0 in Complementary Metal-Oxide-Semiconductor (CMOS) are essential for modern high-speed non-volatile solid-state memories. However, the accumulated storage signal in conventional physical components often leads to data distortion after multiple write operations. This necessitates a write-verify operation to ensure proper values within the 0/1 threshold ranges. In this work, a non-gradual switching memory with two distinct stable resistance levels is introduced, enabled by the asymmetric vertical structure of monolayer vacancy-induced oxidized Ti3C2Tx MXene for efficient carrier trapping and releasing. This non-cumulative resistance effect allows non-volatile memories to attain valid 0/1 logic levels through direct reprogramming, eliminating the need for a write-verify operation. The device exhibits superior performance characteristics, including short write/erase times (100 ns), a large switching ratio (≈3 × 104), long cyclic endurance (>104 cycles), extended retention (>4 × 106 s), and highly resistive stability (>104 continuous write operations). These findings present promising avenues for next-generation resistive memories, offering faster programming speed, exceptional write performance, and streamlined algorithms.
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Affiliation(s)
- Dongchen Tan
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Nan Sun
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Jijie Huang
- School of Materials Engineering, Purdue University, West Lafayette, 47907, USA
| | - Zhaorui Zhang
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Lijun Zeng
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Qikun Li
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, China
| | - Sheng Bi
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Jingyuan Bu
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Yan Peng
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
| | - Qinlei Guo
- Department of Material Science and Engineering, Frederick Seitz Material Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, 61801, USA
| | - Chengming Jiang
- Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China
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17
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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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18
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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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
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, 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
| | - 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
| | - 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
| | - Cong Hu
- 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
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- 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
| | - Mengjie Shao
- 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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19
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Wang W, Wang Y, Yin F, Niu H, Shin YK, Li Y, Kim ES, Kim NY. Tailoring Classical Conditioning Behavior in TiO 2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware. NANO-MICRO LETTERS 2024; 16:133. [PMID: 38411720 PMCID: PMC10899558 DOI: 10.1007/s40820-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/28/2023] [Indexed: 02/28/2024]
Abstract
Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.
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Affiliation(s)
- Wenxiao Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Yaqi Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
| | - Feifei Yin
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Hongsen Niu
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Young-Kee Shin
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Yang Li
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China.
- School of Microelectronics, Shandong University, Jinan, 250101, People's Republic of China.
| | - Eun-Seong Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
| | - Nam-Young Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
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20
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Lee KJ, Kim JH, Jeon S, Shin CW, Kim HR, Park HG, Kim J. Polarization-Dependent Memory and Erasure in Quantum Dots/Graphene Synaptic Devices. NANO LETTERS 2024; 24:2421-2427. [PMID: 38319957 DOI: 10.1021/acs.nanolett.4c00124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
We demonstrate excitatory and inhibitory properties in a single heterostructure consisting of two quantum dots/graphene synaptic elements using linearly polarized monochromatic light. Perovskite quantum dots and PbS quantum dots were used to increase and decrease photocurrent weights, respectively. The polarization-dependent photocurrent was realized by adding a polarizer in the middle of the PbS quantum dots/graphene and perovskite quantum dots/graphene elements. When linearly polarized light passed through the polarizer, both the lower excitatory and upper inhibitory devices were activated, with the lower device with the stronger response dominating to increase the current weight. In contrast, the polarized light was blocked by the polarizer, and the above device was only operated, reducing the current weight. Furthermore, two orthogonal polarizations of light were used to perform the sequential processes of potentiation and habituation. By adjustment of the polarization angle of light, not only the direction of the current weight but also its level was altered.
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Affiliation(s)
- Ki-Jeong Lee
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Jin Hyung Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Sooin Jeon
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Chi Won Shin
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Ha-Reem Kim
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Jungkil Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
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21
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Yang F, Zhang Y, Feng X, Guo J, Cheng G, Du Z. Light and voltage dual-modulated volatile resistive switching in single ZnO nanowires. NANOTECHNOLOGY 2024; 35:185201. [PMID: 38271735 DOI: 10.1088/1361-6528/ad22b1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/24/2024] [Indexed: 01/27/2024]
Abstract
A single ZnO nanowire device with volatile resistive switching behavior has been prepared. Different from traditional resistive switching devices, such ZnO nanowire devices do not exhibit resistive switching behaviors under a single bias voltage, and appear resistive switching behavior under the combined action of light stimuli and bias voltage. Through the demonstration of the time-dependent hysteresis curve and atmosphere-dependent hysteresis loop of the resistive switching devices, it is believed that under the resistive switching process, ultraviolet illumination can increase the carrier concentration and modulate the barrier depletion structure, and external bias voltage can ionize the surface state. They work together to modulate the switching process of the devices. Such light stimuli and bias voltage dual-modulated resistive switching device enables optical control and may thus be considered for sensory applications or optically tunable memories.
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Affiliation(s)
- Feng Yang
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
| | - Yongle Zhang
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
| | - Xue Feng
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
| | - Junmeng Guo
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
| | - Gang Cheng
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
| | - Zuliang Du
- Key Laboratory for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, Collaborative Innovation Center of Nano Functional Materials and Applications, School of Materials Science and Engineering, Henan University, Kaifeng 475004, People's Republic of China
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22
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Yantara N, Ng SE, Sharma D, Zhou B, Sun PSV, Chua HM, Jamaludin NF, Basu A, Mathews N. Ion-Mediated Recombination Dynamics in Perovskite-Based Memory Light-Emitting Diodes for Neuromorphic Control Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305857. [PMID: 37640560 DOI: 10.1002/adma.202305857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/03/2023] [Indexed: 08/31/2023]
Abstract
Neuromorphic devices can help perform memory-heavy tasks more efficiently due to the co-localization of memory and computing. In biological systems, fast dynamics are necessary for rapid communication, while slow dynamics aid in the amplification of signals over noise and regulatory processes such as adaptation- such dual dynamics are key for neuromorphic control systems. Halide perovskites exhibit much more complex phenomena than conventional semiconductors due to their coupled ionic, electronic, and optical properties which result in modulatable drift, diffusion of ions, carriers, and radiative recombination dynamics. This is exploited to engineer a dual-emitter tandem device with the requisite dual slow-fast dynamics. Here, a perovskite-organic tandem light-emitting diode (LED) capable of modulating its emission spectrum and intensity owing to the ion-mediated recombination zone modulation between the green-emitting quasi-2D perovskite layer and the red-emitting organic layer is introduced. Frequency-dependent response and high dynamic range memory of emission intensity and spectra in a LED are demonstrated. Utilizing the emissive read-out, image contrast enhancement as a neuromorphic pre-processing step to improve pattern recognition capabilities is illustrated. As proof of concept using the device's slow-fast dynamics, an inhibition of the return mechanism is physically emulated.
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Affiliation(s)
- Natalia Yantara
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, 50 Nanyang Drive, Singapore, 637553, Singapore
| | - Si En Ng
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Divyam Sharma
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Biyan Zhou
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, Hong Kong
| | - Pao-Sheng Vincent Sun
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, Hong Kong
| | - Huei Min Chua
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Nur Fadilah Jamaludin
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, 50 Nanyang Drive, Singapore, 637553, Singapore
| | - Arindam Basu
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, Hong Kong
| | - Nripan Mathews
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, 50 Nanyang Drive, Singapore, 637553, Singapore
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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23
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Wang P, Li J, Xue W, Ci W, Jiang F, Shi L, Zhou F, Zhou P, Xu X. Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305679. [PMID: 38029338 PMCID: PMC10797471 DOI: 10.1002/advs.202305679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/01/2023] [Indexed: 12/01/2023]
Abstract
The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.
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Affiliation(s)
- Peng Wang
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Jie Li
- School of MicroelectronicsSouthern University of Science and TechnologyShenzhen518000China
| | - Wuhong Xue
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Wenjuan Ci
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Fengxian Jiang
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Lei Shi
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Feichi Zhou
- School of MicroelectronicsSouthern University of Science and TechnologyShenzhen518000China
| | - Peng Zhou
- ASIC & System State Key Lab School of MicroelectronicsFudan UniversityShanghai200433China
| | - Xiaohong Xu
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
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24
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Yamazaki Y, Kinoshita K. Photonic Physical Reservoir Computing with Tunable Relaxation Time Constant. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304804. [PMID: 37984878 PMCID: PMC10797460 DOI: 10.1002/advs.202304804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/27/2023] [Indexed: 11/22/2023]
Abstract
Recent years have witnessed a rising demand for edge computing, and there is a need for methods to decrease the computational cost while maintaining a high learning performance when processing information at arbitrary edges. Reservoir computing using physical dynamics has attracted significant attention. However, currently, the timescale of the input signals that can be processed by physical reservoirs is limited by the transient characteristics inherent to the selected physical system. This study used an Sn-doped In2 O3 /Nb-doped SrTiO3 junction to fabricate a memristor that could respond to both electrical and optical stimuli. The results show that the timescale of the transient current response of the device could be controlled over several orders of magnitude simply by applying a small voltage. The computational performance of the device as a physical reservoir is evaluated in an image classification task, demonstrating that the learning accuracy could be optimized by tuning the device to exhibit appropriate transient characteristics according to the timescale of the input signals. These results are expected to provide deeper insights into the photoconductive properties of strontium titanate, as well as support the physical implementation of computing systems.
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Affiliation(s)
- Yutaro Yamazaki
- Department of Applied PhysicsTokyo University of Science6–3–1 Niijuku, Katsushika‐kuTokyo125–8585Japan
| | - Kentaro Kinoshita
- Department of Applied PhysicsTokyo University of Science6–3–1 Niijuku, Katsushika‐kuTokyo125–8585Japan
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25
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Yang J, Hu L, Shen L, Wang J, Cheng P, Lu H, Zhuge F, Ye Z. Optically driven intelligent computing with ZnO memristor. FUNDAMENTAL RESEARCH 2024; 4:158-166. [PMID: 38933832 PMCID: PMC11197590 DOI: 10.1016/j.fmre.2022.06.019] [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: 04/15/2022] [Revised: 06/13/2022] [Accepted: 06/22/2022] [Indexed: 10/16/2022] Open
Abstract
Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.
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Affiliation(s)
- Jing Yang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Lingxiang Hu
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China
| | - Liufeng Shen
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Jingrui Wang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Peihong Cheng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Huanming Lu
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
| | - Zhizhen Ye
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
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26
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Guo Z, Zhang J, Yang B, Li L, Liu X, Xu Y, Wu Y, Guo P, Sun T, Dai S, Liang H, Wang J, Zou Y, Xiong L, Huang J. Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2310155. [PMID: 38100140 DOI: 10.1002/adma.202310155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.
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Affiliation(s)
- Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ben Yang
- 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
| | - Xu Liu
- 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
| | - Yue Wu
- 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
| | - Tongrui Sun
- 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
| | - Haixia Liang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jun Wang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yidong Zou
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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27
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Liu G, Xue Z, Zhang X, Liu Q, Kuang Y, He M, Xu J, Lv M, Xiu H, Zhai G, Liu D, Xia Y, Dai N, Dai M. Multifunctional Multigate One-Transistor with Thin Advanced Materials, Logic-in-Memory, and Artificial Synaptic Behaviors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:55957-55964. [PMID: 37992220 DOI: 10.1021/acsami.3c10366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
The high device density and fabrication complexity have hampered the development of the electronics. The advanced designs, which could implement the functions of the circuits with higher device density but less fabrication complexity, are hence required. Meanwhile, the MoS2-based devices have recently attracted considerable attention owing to their advantages such as the ultrathin thickness. However, the MoS2-based multifunctional multigate one-transistor (MGT) designs with logic-in-memory and artificial synaptic functions have rarely been reported. Here, an MGT structure based on the MoS2 channel is proposed, with both the logic-in-memory and artificial synaptic behaviors and with more controllable processes than the manual transfer. The proposed MoS2-based MGT functions could be attributed to the semijunction mechanism and enhanced effect of the additional terminals with improved controllability. This study is the first to demonstrate that the neuromorphic computing, logic gate, and memory functions can all be achieved in a MoS2 MGT device without using any additional layers or plasticity to a transistor. The reported results provide a new strategy for developing brain-like systems and next-generation electronics using multifunctional designs and ultrathin materials.
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Affiliation(s)
- Guanyi Liu
- Department of Spine Surgery, Ningbo No. 6 Hospital, 1059 Zhongshandong Road, Ningbo, Zhejiang 315040, People's Republic of China
| | - Zhibiao Xue
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
- School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
| | - Xiaoyang Zhang
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Qitao Liu
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Yongbo Kuang
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Meng He
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
| | - Ji Xu
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Mingming Lv
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Hao Xiu
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Gangpeng Zhai
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Deyu Liu
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
| | - Yang Xia
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
| | - Ning Dai
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Mingzhi Dai
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Energy Division, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang Province 315201, China
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28
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Huang PY, Jiang BY, Chen HJ, Xu JY, Wang K, Zhu CY, Hu XY, Li D, Zhen L, Zhou FC, Qin JK, Xu CY. Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction. Nat Commun 2023; 14:6736. [PMID: 37872169 PMCID: PMC10593955 DOI: 10.1038/s41467-023-42488-9] [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: 10/17/2022] [Accepted: 10/12/2023] [Indexed: 10/25/2023] Open
Abstract
Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.
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Affiliation(s)
- Pei-Yu Huang
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Bi-Yi Jiang
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong-Ji Chen
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Jia-Yi Xu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Kang Wang
- Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Cheng-Yi Zhu
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Xin-Yan Hu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Dong Li
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Liang Zhen
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, China
| | - Fei-Chi Zhou
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Jing-Kai Qin
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
| | - Cheng-Yan Xu
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, China.
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Han MJ, Tsukruk VV. Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors. ACS NANO 2023; 17:18883-18892. [PMID: 37721448 PMCID: PMC10569090 DOI: 10.1021/acsnano.3c04113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.
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Affiliation(s)
- Moon Jong Han
- Department
of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea
| | - Vladimir V. Tsukruk
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
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30
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Rao TS, Mondal I, Bannur B, Kulkarni GU. A scalable solution recipe for a Ag-based neuromorphic device. DISCOVER NANO 2023; 18:124. [PMID: 37812259 PMCID: PMC10562349 DOI: 10.1186/s11671-023-03906-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).
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Affiliation(s)
- Tejaswini S Rao
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore, 560064, India
| | - Indrajit Mondal
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore, 560064, India
| | - Bharath Bannur
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore, 560064, India
| | - Giridhar U Kulkarni
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore, 560064, India.
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31
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Ran Y, Lu W, Wang X, Qin Z, Qin X, Lu G, Hu Z, Zhu Y, Bu L, Lu G. High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks. MATERIALS HORIZONS 2023; 10:4438-4451. [PMID: 37489257 DOI: 10.1039/d3mh00775h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10-7 V, and achieve an ultra-low energy consumption of 2.14 × 10-18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.
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Affiliation(s)
- Yixin Ran
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Wanlong Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Xin Wang
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Zongze Qin
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Xinsu Qin
- School of Chemistry, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China
| | - Guanyu Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Zhen Hu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Yuanwei Zhu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
| | - Laju Bu
- School of Chemistry, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China
| | - Guanghao Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China.
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32
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Wang Y, Yin L, Huang S, Xiao R, Zhang Y, Li D, Pi X, Yang D. Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision. NANO LETTERS 2023; 23:8460-8467. [PMID: 37721358 DOI: 10.1021/acs.nanolett.3c01853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.
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Affiliation(s)
- Yue Wang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Lei Yin
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Shijie Huang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Rulei Xiao
- National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
| | - Yiqiang Zhang
- School of Materials Science and Engineering & College of Chemistry, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Dongke Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Deren Yang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
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33
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Vats G, Hodges B, Ferguson AJ, Wheeler LM, Blackburn JL. Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205459. [PMID: 36120918 DOI: 10.1002/adma.202205459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Metal halide perovskite based materials have emerged over the past few decades as remarkable solution-processable optoelectronic materials with many intriguing properties and potential applications. These emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier-diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, recent progress toward memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality is reviewed.
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Affiliation(s)
- Gaurav Vats
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
- Department of Physics and Astronomy, Katholieke Universiteit Leuven, Celestijnenlaan 200D, Leuven, B-3001, Belgium
| | - Brett Hodges
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | | | - Lance M Wheeler
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
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34
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Yang G, Kim Y, Jeon J, Lee M, Kim D, Kim S, Eom K, Lee H. Reversible Photomodulation of Two-Dimensional Electron Gas in LaAlO 3/SrTiO 3 Heterostructures. NANO LETTERS 2023. [PMID: 37418557 DOI: 10.1021/acs.nanolett.3c01076] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Long-lived photoinduced conductance changes in LaAlO3/SrTiO3 (LAO/STO) heterostructures enable their use in optoelectronic memory applications. However, it remains challenging to quench the persistent photoconductivity (PPC) instantly and reproducibly, which limits the reversible optoelectronic switching. Herein, we demonstrate a reversible photomodulation of two-dimensional electron gas (2DEG) in LAO/STO heterostructures with high reproducibility. By irradiating UV pulses, the 2DEG at the LAO/STO interface is gradually transformed to the PPC state. Notably, the PPC can be completely removed by water treatment when two key requirements are met: (1) the moderate oxygen deficiency in STO and (2) the minimal band edge fluctuation at the interface. Through our X-ray photoelectron spectroscopy and electrical noise analysis, we reveal that the reproducible change in the conductivity of 2DEG is directly attributed to the surface-driven electron relaxation in the STO. Our results provide a stepping-stone toward developing optically tunable memristive devices based on oxide 2DEG systems.
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Affiliation(s)
- Gyeongmo Yang
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Youngmin Kim
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Jaeyoung Jeon
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Minkyung Lee
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Doyeop Kim
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Kitae Eom
- School of Advanced Materials science and Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
| | - Hyungwoo Lee
- Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea
- Department of Physics, Ajou University, Suwon 16499, Republic of Korea
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35
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López C. Artificial Intelligence and Advanced Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208683. [PMID: 36560859 DOI: 10.1002/adma.202208683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/01/2022] [Indexed: 06/09/2023]
Abstract
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
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Affiliation(s)
- Cefe López
- Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la Cruz 3, Madrid, 28049, Spain
- Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizábal 4, San Sebastián, 20018, España
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36
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El Hage R, Humbert V, Rouco V, Sánchez-Santolino G, Lagarrigue A, Seurre K, Carreira SJ, Sander A, Charliac J, Mesoraca S, Trastoy J, Briatico J, Santamaría J, Villegas JE. Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction. Nat Commun 2023; 14:3010. [PMID: 37230971 DOI: 10.1038/s41467-023-38608-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.
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Affiliation(s)
- Ralph El Hage
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Vincent Humbert
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Victor Rouco
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Gabriel Sánchez-Santolino
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Aurelien Lagarrigue
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Kevin Seurre
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Santiago J Carreira
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Anke Sander
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jérôme Charliac
- Laboratoire de Physique des Interfaces et des Couches Minces (UMR7647), CNRS, Ecole Polytechnique, 91128, Palaiseau Cedex, France
| | - Salvatore Mesoraca
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Javier Briatico
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jacobo Santamaría
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Javier E Villegas
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
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Wei Y, Liu Y, Lin Q, Liu T, Wang S, Chen H, Li C, Gu X, Zhang X, Huang H. Organic Optoelectronic Synapses for Sound Perception. NANO-MICRO LETTERS 2023; 15:133. [PMID: 37221281 PMCID: PMC10205940 DOI: 10.1007/s40820-023-01116-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/24/2023] [Indexed: 05/25/2023]
Abstract
The neuromorphic systems for sound perception is under highly demanding for the future bioinspired electronics and humanoid robots. However, the sound perception based on volume, tone and timbre remains unknown. Herein, organic optoelectronic synapses (OOSs) are constructed for unprecedented sound recognition. The volume, tone and timbre of sound can be regulated appropriately by the input signal of voltages, frequencies and light intensities of OOSs, according to the amplitude, frequency, and waveform of the sound. The quantitative relation between recognition factor (ζ) and postsynaptic current (I = Ilight - Idark) is established to achieve sound perception. Interestingly, the bell sound for University of Chinese Academy of Sciences is recognized with an accuracy of 99.8%. The mechanism studies reveal that the impedance of the interfacial layers play a critical role in the synaptic performances. This contribution presents unprecedented artificial synapses for sound perception at hardware levels.
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Affiliation(s)
- Yanan Wei
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Youxing Liu
- School of Materials Science and Engineering, Peking University, Beijing, 100871, People's Republic of China
| | - Qijie Lin
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Tianhua Liu
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Song Wang
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Hao Chen
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Congqi Li
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Xiaobin Gu
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Xin Zhang
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
| | - Hui Huang
- College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
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38
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Zhang Y, Huang Z, Jiang J. Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2186689. [PMID: 37007672 PMCID: PMC10054230 DOI: 10.1080/14686996.2023.2186689] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.
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Affiliation(s)
- Yi Zhang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Zhuohui Huang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
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Sharma D, Rao D, Saha B. A photonic artificial synapse with a reversible multifaceted photochromic compound. NANOSCALE HORIZONS 2023; 8:543-549. [PMID: 36852974 DOI: 10.1039/d2nh00532h] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Modern computational technology based on the von Neumann architecture physically partitions memory and the central processing unit, resulting in fundamental speed limitations and high energy consumption. On the other hand, the human brain is an extraordinary multifunctional organ composed of more than a billion neurons capable of simultaneously thinking, processing, and storing information. Neurons are interconnected with synapses that control information flow from pre-synaptic-to-post-synaptic neurons. Therefore, emulating synaptic functionalities and developing neuromorphic computational architecture has recently attracted much interest. Due to their high-speed, large bandwidth, and no interconnect-related power loss, photonic (all-optical) synapses can overcome the existing hurdles with electronic synapses. Here, we show an artificial photonic synapse by utilizing the well-established reversible, high-contrast photochromic organic compound, spiropyran, stimulated by optical pulses. Optical transmission of spiropyran significantly changes during spiropyran-merocyanine isomerization driven by UV-visible optical pulses. Such changes are equivalent to the biological synapses' inhibitory and excitatory synaptic actions. The slow relaxation to the initial state is considered as synaptic plasticity responsible for learning and memory formation. Short-term memory (STM), long-term memory (LTM), and transition from the STM to the LTM are demonstrated in all-optical synapses by modulating the stimuli's strength. The solvatochromic properties of spiropyran are further utilized to augment memory in synapses. Our work shows that photochromic organic compounds are excellent hosts for artificial photonic synapses and can be implemented in neuromorphic applications.
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Affiliation(s)
- Deeksha Sharma
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
- International Centre for Materials Science, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Dheemahi Rao
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
- International Centre for Materials Science, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Bivas Saha
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
- International Centre for Materials Science, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
- School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
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40
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Dong X, Li S, Sun H, Jian L, Wei W, Chen J, Zhao Y, Chen J, Zhang X, Li Y. Optoelectronic Memristive Synapse Behavior for the Architecture of Cu 2ZnSnS 4@BiOBr Embedded in Poly(methyl methacrylate). J Phys Chem Lett 2023; 14:1512-1520. [PMID: 36745109 DOI: 10.1021/acs.jpclett.2c03939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu2ZnSnS4@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >104 s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.
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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, Lanzhou730070, China
| | - Siyuan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Hao Sun
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Lijuan Jian
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Wenbin Wei
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
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Kim JH, Stolte M, Würthner F. Wavelength and Polarization Sensitive Synaptic Phototransistor Based on Organic n-type Semiconductor/Supramolecular J-Aggregate Heterostructure. ACS NANO 2022; 16:19523-19532. [PMID: 36356301 DOI: 10.1021/acsnano.2c09747] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Human retina- and brain-inspired optoelectronic synapses, which integrate light detection and signal memory functions for data processing, have significant interest because of their potential applications for artificial vision technology. In nature, many animals such as mantis shrimp use polarized light information as well as scalar information including wavelength and intensity; however, a spectropolarimetric organic optoelectronic synapse has been seldom investigated. Herein, we report an organic synaptic phototransistor, consisting of a charge trapping liquid-crystalline perylene bisimide J-aggregate and a charge transporting crystalline dichlorinated naphthalene diimide, that can detect both wavelength and polarization information. The device shows persistent positive and negative photocurrents under low and high voltage conditions, respectively. Furthermore, the aligned organic heterostructure in the thin-film enables linearly polarized light to be absorbed with a dichroic ratio of 1.4 and 3.7 under transverse polarized blue and red light illumination, respectively. These features allow polarized light sensitive postsynaptic functions in the device. Consequently, a simple polarization imaging sensor array is successfully demonstrated using photonic synapses, which suggests that a supramolecular material is an important candidate for the development of spectropolarimetric neuromorphic vision systems.
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Affiliation(s)
- Jin Hong Kim
- Center for Nanosystems Chemistry (CNC) and Bavarian Polymer Institute (BPI), Universität Würzburg, 97074 Würzburg, Germany
| | - Matthias Stolte
- Center for Nanosystems Chemistry (CNC) and Bavarian Polymer Institute (BPI), Universität Würzburg, 97074 Würzburg, Germany
- Institut für Organische Chemie, Universität Würzburg, 97074 Würzburg, Germany
| | - Frank Würthner
- Center for Nanosystems Chemistry (CNC) and Bavarian Polymer Institute (BPI), Universität Würzburg, 97074 Würzburg, Germany
- Institut für Organische Chemie, Universität Würzburg, 97074 Würzburg, Germany
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Wu F, Chou CH, Tseng TY. CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing. NANOSCALE RESEARCH LETTERS 2022; 17:105. [PMID: 36342556 PMCID: PMC9640510 DOI: 10.1186/s11671-022-03744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.
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Affiliation(s)
- Facai Wu
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Chien-Hung Chou
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Tseung-Yuen Tseng
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan.
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43
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Zhang Z, Zhao X, Zhang X, Hou X, Ma X, Tang S, Zhang Y, Xu G, Liu Q, Long S. In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array. Nat Commun 2022; 13:6590. [PMID: 36329017 PMCID: PMC9633641 DOI: 10.1038/s41467-022-34230-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.
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Affiliation(s)
- Zhongfang Zhang
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Xiaolong Zhao
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Xumeng Zhang
- grid.8547.e0000 0001 0125 2443Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Xiaohu Hou
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Xiaolan Ma
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Shuangzhu Tang
- grid.8547.e0000 0001 0125 2443Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Ying Zhang
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Guangwei Xu
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
| | - Qi Liu
- grid.8547.e0000 0001 0125 2443Frontier Institute of Chip and System, Fudan University, Shanghai, China
| | - Shibing Long
- grid.59053.3a0000000121679639School of Microelectronics, University of Science and Technology of China, Hefei, China
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44
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Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
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Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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45
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In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat Commun 2022; 13:5223. [PMID: 36064944 PMCID: PMC9445171 DOI: 10.1038/s41467-022-32790-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction. Designing in-sensor computing systems remains a challenge. Here, the authors demonstrate artificial optical neurons based on the in-sensor computing architecture that fuses sensory and computing nodes into a single platform capable of reducing data transfer time and energy for encoding and classification.
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46
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Mennel L, Molina-Mendoza AJ, Paur M, Polyushkin DK, Kwak D, Giparakis M, Beiser M, Andrews AM, Mueller T. A photosensor employing data-driven binning for ultrafast image recognition. Sci Rep 2022; 12:14441. [PMID: 36002539 PMCID: PMC9402579 DOI: 10.1038/s41598-022-18821-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/19/2022] [Indexed: 11/11/2022] Open
Abstract
Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single “superpixel” that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.
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Affiliation(s)
- Lukas Mennel
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria
| | - Aday J Molina-Mendoza
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria
| | - Matthias Paur
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria
| | - Dmitry K Polyushkin
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria
| | - Dohyun Kwak
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria
| | - Miriam Giparakis
- Institute of Solid-State Electronics, Vienna University of Technology, Gußhausstraße 25, 1040, Vienna, Austria
| | - Maximilian Beiser
- Institute of Solid-State Electronics, Vienna University of Technology, Gußhausstraße 25, 1040, Vienna, Austria
| | - Aaron Maxwell Andrews
- Institute of Solid-State Electronics, Vienna University of Technology, Gußhausstraße 25, 1040, Vienna, Austria
| | - Thomas Mueller
- Institute of Photonics, Vienna University of Technology, Gußhausstraße 27-29, 1040, Vienna, Austria.
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47
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Guo J, Liu L, Bian B, Wang J, Zhao X, Zhang Y, Yan Y. Field-Created Coordinate Cation Bridges Enable Conductance Modulation and Artificial Synapse within Metal Nanoparticles. NANO LETTERS 2022; 22:6794-6801. [PMID: 35939405 DOI: 10.1021/acs.nanolett.2c02675] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
When metal nanoparticles are functionalized with charged ligands, the movement of counterions and conduction electrons is coupled, which enables us to develop electronic devices, including diodes, transistors, and logic gates, but dynamically modulating the conductivity of a synaptic device within these materials has proved challenging. Here we show that an artificial synapse can be created from thin films of functionalized metal nanoparticles using an active silver electrode. The electric-field-injected Ag+ coordinates with carboxyl ligands that sets up a conduction bridge to increase the nanoparticle conductivity by reducing the electron tunneling/hopping energy barriers. The dynamic modulation of conductivity allows us to implement several important synaptic functions such as potentiation/depression, paired-pulse facilitation, learning behaviors including short-term to long-term memory transition, self-learning, and massed leaning vs spaced learning. Finally, based on the nonvolatile characteristics, the metal nanoparticle synapse is used to build a single-layer hardware spiking neural network (SNN) for pattern recognition.
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Affiliation(s)
- Jiahui Guo
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Lin Liu
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Baoan Bian
- School of Science, Jiangnan University, Wuxi 214122, People's Republic of China
| | - Jingyu Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Xing Zhao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
| | - Yuchun Zhang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
| | - Yong Yan
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
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48
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Li Q, Wang T, Fang Y, Hu X, Tang C, Wu X, Zhu H, Ji L, Sun QQ, Zhang DW, Chen L. Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing. NANO LETTERS 2022; 22:6435-6443. [PMID: 35737934 DOI: 10.1021/acs.nanolett.2c01768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.
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Affiliation(s)
- Qingxuan Li
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Tianyu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Yuqing Fang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xuemeng Hu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Chengkang Tang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xiaohan Wu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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Yu H, Zhu Y, Zhu L, Lin X, Wan Q. Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2022.954165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.
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50
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