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Xu K, Wang T, Lu C, Song Y, Liu Y, Yu J, Liu Y, Li Z, Meng J, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing. NANO LETTERS 2024. [PMID: 39148056 DOI: 10.1021/acs.nanolett.4c02142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.
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Affiliation(s)
- Kangli Xu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Tianyu Wang
- School of Integrated Circuits, Shandong University, Jinan 250100, China
- Suzhou Research Institute of Shandong University, Suzhou 215123, China
- State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
| | - Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yifan Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yongkai Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yinchi Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Zhenhai Li
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
- Jiashan Fudan Institute, Jiaxing 314110, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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Li S, Gao L, Liu C, Guo H, Yu J. Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors. SENSORS (BASEL, SWITZERLAND) 2024; 24:4915. [PMID: 39123962 PMCID: PMC11314768 DOI: 10.3390/s24154915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 07/26/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024]
Abstract
Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.
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Affiliation(s)
| | | | | | | | - Junsheng Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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Zhang QR, Ouyang WL, Wang XM, Yang F, Chen JG, Wen ZX, Liu JX, Wang G, Liu Q, Liu FC. Dynamic memristor for physical reservoir computing. NANOSCALE 2024; 16:13847-13860. [PMID: 38984618 DOI: 10.1039/d4nr01445f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.
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Affiliation(s)
- Qi-Rui Zhang
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei-Lun Ouyang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xue-Mei Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fan Yang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jian-Gang Chen
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhi-Xing Wen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jia-Xin Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ge Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qing Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Cai Liu
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
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4
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Hu J, Li H, Zhang Y, Zhou J, Zhao Y, Xu Y, Yu B. Reconfigurable Neuromorphic Computing with 2D Material Heterostructures for Versatile Neural Information Processing. NANO LETTERS 2024. [PMID: 39038296 DOI: 10.1021/acs.nanolett.4c02658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Reconfigurable neuromorphic computing holds promise for advancing energy-efficient neural network implementation and functional versatility. Previous work has focused on emulating specific neural functions rather than an integrated approach. We propose an all two-dimensional (2D) material-based heterostructure capable of performing multiple neuromorphic operations by reconfiguring output terminals in response to stimuli. Specifically, our device can synergistically emulate the key neural elements of the synapse, neuron, and dendrite, which play important and interrelated roles in information processing. Dendrites, the branches that receive and transmit presynaptic action potentials, possess the ability to nonlinearly integrate and filter incoming signals. The proposed heterostructure allows reconfiguration between different operation modes, demonstrating its potential for diverse computing tasks. As a proof of concept, we show that the device can perform basic Boolean logic functions. This highlights its applicability to complex neural-network-based information processing problems. Our integrated neuromorphic approach may advance the development of versatile, low-power neuromorphic hardware.
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Affiliation(s)
- Jiayang Hu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Hanxi Li
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yishu Zhang
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Jiachao Zhou
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yuda Zhao
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yang Xu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Bin Yu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
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5
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Wu Y, Yang H, He Q, Jiang H, Chen W, Tan C, Zhang Y, Zheng Y. The Investigation of Neuromimetic Dynamics in Ferroelectrics via In Situ TEM. NANO LETTERS 2024. [PMID: 38825790 DOI: 10.1021/acs.nanolett.4c01626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The core task of neuromorphic devices is to effectively simulate the behavior of neurons and synapses. Based on the functionality of ferroelectric domains with the advantages of low power consumption and high-speed response, great progress has been made in realizing neuromimetic behaviors such as ferroelectric synaptic devices. However, the correlation between the ferroelectric domain dynamics and neuromimetic behavior remains unclear. Here, we reveal the correlation between domain/domain wall dynamics and neuromimetic behaviors from a microscopic perspective in real-time by using high temporal and spatial resolution in situ transmission electron microscopy. Furthermore, we propose utilizing ferroelectric microstructures for the simultaneous simulation of neuronal and synaptic plasticity, which is expected to improve the integration and performance of ferroelectric neuromorphic devices. We believe that this work to study neuromimetic behavior from the perspective of domain dynamics is instructive for the development of ferroelectric neuromorphic devices.
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Affiliation(s)
- Yiwei Wu
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Hui Yang
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Qian He
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - He Jiang
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Weijin Chen
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Congbing Tan
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Hunan Provincial Key Laboratory of Intelligent Sensors and Sensor Materials, School of Physics and Electronics, Hunan University of Science and Technology, Xiangtan 411201, People's Republic of China
| | - Yi Zhang
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Yue Zheng
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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6
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Chen K, Pan K, He S, Liu R, Zhou Z, Zhu D, Liu Z, He Z, Sun H, Wang M, Wang K, Tang M, Liu J. Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400966. [PMID: 38483027 PMCID: PMC11109647 DOI: 10.1002/advs.202400966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Indexed: 05/23/2024]
Abstract
Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.
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Affiliation(s)
- Kang Chen
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Shang He
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Rui Liu
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Zhe Zhou
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Duoyi Zhu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zhengdong Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zixi He
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Hongchao Sun
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Min Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Kaili Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Minghua Tang
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
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Li T, Wu Y, Yu G, Li S, Ren Y, Liu Y, Liu J, Feng H, Deng Y, Chen M, Zhang Z, Min T. Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP 2S 6. Nat Commun 2024; 15:2653. [PMID: 38531845 DOI: 10.1038/s41467-024-46891-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Realization of higher-order multistates with mutual interstate switching in ferroelectric materials is a perpetual drive for high-density storage devices and beyond-Moore technologies. Here we demonstrate experimentally that antiferroelectric van der Waals CuInP2S6 films can be controllably stabilized into double, quadruple, and sextuple polarization states, and a system harboring polarization order of six is also reversibly tunable into order of four or two. Furthermore, for a given polarization order, mutual interstate switching can be achieved via moderate electric field modulation. First-principles studies of CuInP2S6 multilayers help to reveal that the double, quadruple, and sextuple states are attributable to the existence of respective single, double, and triple ferroelectric domains with antiferroelectric interdomain coupling and Cu ion migration. These findings offer appealing platforms for developing multistate ferroelectric devices, while the underlining mechanism is transformative to other non-volatile material systems.
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Affiliation(s)
- Tao Li
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China
| | - Yongyi Wu
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China
| | - Guoliang Yu
- Key Laboratory for Matter Microstructure and Function of Hunan Province, Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Synergetic Innovation Centre for Quantum Effects and Applications (SICQEA), School of Physics and Electronics, Hunan Normal University, 410081, Changsha, China
| | - Shengxian Li
- Key Laboratory for Matter Microstructure and Function of Hunan Province, Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Synergetic Innovation Centre for Quantum Effects and Applications (SICQEA), School of Physics and Electronics, Hunan Normal University, 410081, Changsha, China
| | - Yifeng Ren
- Solid State Microstructure National Key Lab and Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Yadong Liu
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China
| | - Jiarui Liu
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China
| | - Hao Feng
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China
| | - Yu Deng
- Solid State Microstructure National Key Lab and Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Mingxing Chen
- Key Laboratory for Matter Microstructure and Function of Hunan Province, Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Synergetic Innovation Centre for Quantum Effects and Applications (SICQEA), School of Physics and Electronics, Hunan Normal University, 410081, Changsha, China.
- State Key Laboratory of Powder Metallurgy, Central South University, 410083, Changsha, China.
| | - Zhenyu Zhang
- International Center for Quantum Design of Functional Materials (ICQD) and Hefei National Laboratory, University of Science and Technology of China, 230026, Hefei, Anhui, China.
| | - Tai Min
- Centre for Spintronics and Quantum Systems, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, 710049, Xi'an, China.
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8
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Li T, Miao J, Fu X, Song B, Cai B, Ge X, Zhou X, Zhou P, Wang X, Jariwala D, Hu W. Reconfigurable, non-volatile neuromorphic photovoltaics. NATURE NANOTECHNOLOGY 2023; 18:1303-1310. [PMID: 37474683 DOI: 10.1038/s41565-023-01446-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/08/2023] [Indexed: 07/22/2023]
Abstract
The neural network image sensor-which mimics neurobiological functions of the human retina-has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrent with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional semiconducting metal sulfides that is concurrently a photovoltaic detector. The device is based on a metal-semiconductor-metal (MSM) two-terminal structure with pulse-tunable sulfur vacancies at the M-S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent can be changed with multiple stable magnitudes. The bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network is finally designed for image processing and object detection using the same device. The results demonstrated that neuromorphic photodetectors can be the key components of visual perception hardware.
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Affiliation(s)
- Tangxin Li
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jinshui Miao
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Xiao Fu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Song
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, China
| | - Bin Cai
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, China
| | - Xun Ge
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Xiaohao Zhou
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Zhou
- School of Microelectronics, Fudan University, Shanghai, China
| | - Xinran Wang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Weida Hu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
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9
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Han J, Shan X, Lin Y, Tao Y, Zhao X, Wang Z, Xu H, Liu Y. Multi-Wavelength-Recognizable Memristive Devices via Surface Plasmon Resonance Effect for Color Visual System. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207928. [PMID: 36890789 DOI: 10.1002/smll.202207928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/04/2023] [Indexed: 06/08/2023]
Abstract
Photoelectric memristor has attracted many attentions thanks to their promising potential in optical communication chips and artificial vision systems. However, the implementation of an artificial visual system based on memristive devices remains a considerable challenge because most photoelectric memristors cannot recognize color. Herein, multi-wavelength recognizable memristive devices based on silver(Ag) nanoparticles (NPs) and porous silicon oxide (SiOx ) nanocomposites are presented. Rely on the effects of localized surface plasmon resonance (LSPR) and optical excitation of Ag NPs in SiOx , the set voltage of the device can be gradually reduced. Moreover, the current overshoot problem is alleviated to suppress conducting filament overgrowth after visible light irradiation with different wavelengths, resulting in diverse low resistance states (LRS). Taking advantage of the characteristics of controlled switching voltage and LRS resistance distribution, color image recognition is finally realized in the present work. X-ray photoelectron spectroscopy (XPS) and conductive atomic force microscopy (C-AFM) show that the light irradiation plays an important role on resistive switching (RS) process: the photo-assisted Ag ionization leads to a significant reduction of set voltage and overshoot current. This work provides an effective method toward the development of multi-wavelength-recognizable memristive devices for future artificial color vision system.
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Affiliation(s)
- Jiaqi Han
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Xuanyu Shan
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Ye Tao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
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10
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Liu X, Wang S, Di Z, Wu H, Liu C, Zhou P. An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301851. [PMID: 37229772 PMCID: PMC10401094 DOI: 10.1002/advs.202301851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/26/2023] [Indexed: 05/27/2023]
Abstract
Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.
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Affiliation(s)
- Xiaoxian Liu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Shuiyuan Wang
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Ziye Di
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Haoqi Wu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Chunsen Liu
- Frontier Institute of Chip and System & Qizhi Institute, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
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11
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Li Z, Tang W, Zhang B, Yang R, Miao X. Emerging memristive neurons for neuromorphic computing and sensing. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2188878. [PMID: 37090846 PMCID: PMC10120469 DOI: 10.1080/14686996.2023.2188878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Wei Tang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Rui Yang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
- CONTACT Rui Yang School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan430074, China; Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
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12
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Huo J, Yin H, Zhang Y, Tan X, Mao Y, Zhang C, Zhang F, Zhan G, Zhang Z, Zhang Q, Xu G, Wu Z. Quasi-Volatile MoS 2 Barristor Memory for 1T Compact Neuron by Correlative Charges Trapping and Schottky Barrier Modulation. ACS APPLIED MATERIALS & INTERFACES 2022; 14:57440-57448. [PMID: 36512440 DOI: 10.1021/acsami.2c18561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Artificial neurons as the basic units of spiking neural network (SNN) have attracted increasing interest in energy-efficient neuromorphic computing. 2D transition metal dichalcogenide (TMD)-based devices have great potential for high-performance and low-power artificial neural devices, owing to their unique ion motion, interface engineering, and resistive switching behaviors. Although there are widespread applications of TMD-based artificial synapses in neural networks, TMD-based neurons are seldom reported due to the lack of bio-plausible multi-mechanisms to mimic leaking, integrating, and firing biological behaviors without external assistance. In this work, for the first time, a methodology is developed by introducing the hybrid effect of charge trapping (CT) and Schottky barrier (SB) in MoS2 FETs for barristor memory and one-transistor (1T) compact artificial neuron realization. By correlating the CT and SB processes, quasi-volatile and resistive switching behaviors are realized on the fabricated MoS2 FET and utilized to mimic the accumulating, leaking, and firing biological behaviors of neurons. Therefore, based on a single quasi-volatile CT-SB MoS2 barristor memory, a 1T compact neuron of the basic leaky-integral-and-fire (LIF) function is demonstrated without a peripheral circuit. Furthermore, a spiking neural network (SNN) based on the CT-SB MoS2 barristor neurons is simulated and implemented in pattern classification with high accuracy approaching 95.82%. This work provides a highly integrated and inherently low-energy implementation for neural networks.
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Affiliation(s)
- Jiali Huo
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Huaxiang Yin
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Yadong Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Xiaosi Tan
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Yunwei Mao
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Chuan Zhang
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Fan Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Guohui Zhan
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Zhaohao Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Qingzhu Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Gaobo Xu
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Zhenhua Wu
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
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Su W, Zhang S, Liu C, Tian Q, Liu X, Li K, Lv Y, Liao L, Zou X. Interlayer Transition Induced Infrared Response in ReS 2/2D Perovskite van der Waals Heterostructure Photodetector. NANO LETTERS 2022; 22:10192-10199. [PMID: 36475758 DOI: 10.1021/acs.nanolett.2c04328] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The emerging Ruddlesden-Popper two-dimensional perovskite (2D PVK) has recently joined the family of 2D semiconductors as a potential competitor for building van der Waals (vdW) heterostructures in future optoelectronics. However, to date, most of the reported heterostructures based on 2D PVKs suffer from poor spectral response that is caused by intrinsic wide bandgap of constituting materials. Herein, a direct heterointerface bandgap (∼0.4 eV) between 2D PVK and ReS2 is demonstrated. The strong interlayer coupling reduces the energy interval at the heterojunction region so that the heterostructure shows high sensitivity with the spectral response expanding to 2000 nm. The large type-II band offsets exceeding 1.1 eV ensure fast photogenerated carriers separation at the heterointerface. When this heterostructure is used as a self-driven photodetector, it exhibits a record high detectivity up to 1.8 × 1014 Jones, surpassing any reported 2D self-driven devices, and an impressive external quantum efficiency of 68%.
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Affiliation(s)
- Wanhan Su
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
| | - Sen Zhang
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
| | - Chang Liu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
| | - Qianlei Tian
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
| | - Xingqiang Liu
- State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha410082, China
| | - Kenli Li
- China National Supercomputing Center in Changsha, HunanChangsha410082, China
| | - Yawei Lv
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
| | - Lei Liao
- State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha410082, China
- School of Physics and Electronic Engineering, Harbin Normal University, Harbin150025, China
| | - Xuming Zou
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha410082, China
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