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Kim D, Truong PL, Lee CB, Bang H, Choi J, Ham S, Ko JH, Kim K, Lee D, Park HJ. Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2402961. [PMID: 38895971 DOI: 10.1002/smll.202402961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/29/2024] [Indexed: 06/21/2024]
Abstract
Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.
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Affiliation(s)
- Dohyung Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Phuoc Loc Truong
- Department of Mechanical Engineering, Gachon University, Gyeonggi, 13120, South Korea
| | - Cheong Beom Lee
- Department of Chemical Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Hyeonsu Bang
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jia Choi
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Seokhyun Ham
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
| | - Jong Hwan Ko
- College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Kyeounghak Kim
- Department of Chemical Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Daeho Lee
- Department of Mechanical Engineering, Gachon University, Gyeonggi, 13120, South Korea
| | - Hui Joon Park
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, South Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, South Korea
- Department of Semiconductor Engineering, Hanyang University, Seoul, 04763, South Korea
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Heo J, Kim S, Kim S, Kim MH. Configurable Synaptic and Stochastic Neuronal Functions in ZnTe-Based Memristor for an RBM Neural Network. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2405768. [PMID: 39236315 DOI: 10.1002/advs.202405768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 07/13/2024] [Indexed: 09/07/2024]
Abstract
This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (µA) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.
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Affiliation(s)
- Jungang Heo
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Seongmin Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Min-Hwi Kim
- School of Electrical and Electronics Engineering and Department of Intelligent Semiconductor Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
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Guo Y, Xie Y, Wang C, Ma J. Energy and synchronization between two neurons with nonlinear coupling. Cogn Neurodyn 2024; 18:1835-1847. [PMID: 39104692 PMCID: PMC11297878 DOI: 10.1007/s11571-023-10044-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/26/2023] [Accepted: 11/26/2023] [Indexed: 08/07/2024] Open
Abstract
Consensus and synchronous firing in neural activities are relative to the physical properties of synaptic connections. For coupled neural circuits, the physical properties of coupling channels control the synchronization stability, and transient period for keeping energy diversity. Linear variable coupling results from voltage coupling via linear resistor by consuming certain Joule heat, and electric synapse coupling between neurons derives from gap junction connection under special electrophysiological condition. In this work, a voltage-controlled electric component with quadratic relation in the i-v (current-voltage) is used to connect two neural circuits composed of two variables. The energy function obtained by using Helmholtz theorem is consistent with the Hamilton energy function converted from the field energy in the neural circuit. Chaotic signals are encoded to approach a mixed signal within certain frequency band, and then its amplitude is adjusted to excite the neuron for detecting possible occurrence of nonlinear resonance. External stimuli are changed to trigger different firing modes, and nonlinear coupling activates changeable coupling intensity. It is confirmed that nonlinear coupling behaves functional regulation as hybrid synapse, and the synchronization transition between neurons can be controlled for reaching possible energy balance. The nonlinear coupling is helpful to keep energy diversity and prevent synchronous bursting because positive and negative feedback is switched with time. As a result, complete synchronization is suppressed and phase lock is controlled between neurons with energy diversity.
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Affiliation(s)
- Yitong Guo
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Ying Xie
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Chunni Wang
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Ma
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050 China
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 430065 China
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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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Yu C, Li S, Pan Z, Liu Y, Wang Y, Zhou S, Gao Z, Tian H, Jiang K, Wang Y, Zhang J. Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor. NANO LETTERS 2024; 24:1620-1628. [PMID: 38277130 DOI: 10.1021/acs.nanolett.3c04193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.
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Affiliation(s)
- Chenglin Yu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Shaorui Li
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhoujie Pan
- XingJian College, Tsinghua University, Beijing 100084, China
| | - Yanming Liu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yongchao Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - Siyi Zhou
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhiting Gao
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - He Tian
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Kaili Jiang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Tsinghua-Foxconn Nanotechnology Research Center, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Yayu Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
| | - Jinsong Zhang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
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6
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Rezaei Y, Khan T, Lee S, Mossé D. Solar-powered Parking Analytics System Using Deep Reinforcement Learning. ACM TRANSACTIONS ON SENSOR NETWORKS 2023; 19:1-27. [DOI: 10.1145/3584949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 02/16/2023] [Indexed: 09/01/2023]
Abstract
Advances in deep vision techniques and the ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as deep learning techniques are power-hungry. In this article, we focus on a parking video analytics platform and propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate the cameras to reduce the energy footprint while retaining the system’s utility. Our key insight is that many video-analytics applications do not need to be always operational, and we can design policies to activate video analytics only when necessary. We design two modes of operation for the reinforcement learning (RL) controller: (i) cloud-based mode and (ii) grid-isolated solar-powered mode. In the cloud-based mode, the controller runs on the cloud to control the cameras, whereas, in the solar-powered mode, the RL controller is constrained by the energy produced by solar. We evaluate our approach on a city-scale parking dataset having 76 streets spread across a city. Our analysis shows RL-CamSleep can learn an adaptive policy that reduces the average energy consumption by 76% and achieves an average accuracy of 98%. For the grid-isolated mode, RL-CamSleep outperforms other baseline techniques demonstrating the need for adaptive policy in energy-constrained environments.
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Affiliation(s)
- Yoones Rezaei
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Talha Khan
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Stephen Lee
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Daniel Mossé
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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7
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Li J, Abbas H, Ang DS, Ali A, Ju X. Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era. NANOSCALE HORIZONS 2023; 8:1456-1484. [PMID: 37615055 DOI: 10.1039/d3nh00180f] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
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Affiliation(s)
- Jiayi Li
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Haider Abbas
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Diing Shenp Ang
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Asif Ali
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Xin Ju
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634
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Zhao J, Ran Y, Pei Y, Wei Y, Sun J, Zhang Z, Wang J, Zhou Z, Wang Z, Sun Y, Yan X. Memristors based on NdNiO 3 nanocrystals film as sensory neurons for neuromorphic computing. MATERIALS HORIZONS 2023; 10:4521-4531. [PMID: 37555245 DOI: 10.1039/d3mh00835e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.
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Affiliation(s)
- Jianhui Zhao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yunfeng Ran
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yifei Pei
- Hebei Key Laboratory of Optic-Electronic Information Materials, College of Physics Science and Technology, Hebei University, Baoding 071002, People's Republic of China
| | - Yiheng Wei
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiameng Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zixuan Zhang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiacheng Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhenyu Zhou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhongrong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yong Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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Chen S, Zhang T, Tappertzhofen S, Yang Y, Valov I. Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301924. [PMID: 37199224 DOI: 10.1002/adma.202301924] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.
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Affiliation(s)
- Shaochuan Chen
- Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Stefan Tappertzhofen
- Chair for Micro- and Nanoelectronics, Department of Electrical Engineering and Information Technology, TU Dortmund University, Martin-Schmeisser-Weg 4-6, D-44227, Dortmund, Germany
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China
| | - Ilia Valov
- Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
- Institute of Electrochemistry and Energy Systems "Acad. E. Budewski", Bulgarian Academy of Sciences, Acad. G. Bonchev 10, 1113, Sofia, Bulgaria
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11
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Yang F, Xu Y, Ma J. A memristive neuron and its adaptability to external electric field. CHAOS (WOODBURY, N.Y.) 2023; 33:023110. [PMID: 36859211 DOI: 10.1063/5.0136195] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Connecting memristors into any neural circuit can enhance its potential controllability under external physical stimuli. Memristive current along a magnetic flux-controlled memristor can estimate the effect of electromagnetic induction on neural circuits and neurons. Here, a charge-controlled memristor is incorporated into one branch circuit of a simple neural circuit to estimate the effect of an external electric field. The field energy kept in each electric component is respectively calculated, and equivalent dimensionless energy function H is obtained to discern the firing mode dependence on the energy from capacitive, inductive, and memristive channels. The electric field energy HM in a memristive channel occupies the highest proportion of Hamilton energy H, and neurons can present chaotic/periodic firing modes because of large energy injection from an external electric field, while bursting and spiking behaviors emerge when magnetic field energy HL holds maximal proportion of Hamilton energy H. The memristive current is modified to control the firing modes in this memristive neuron accompanying with a parameter shift and shape deformation resulting from energy accommodation in the memristive channel. In the presence of noisy disturbance from an external electric field, stochastic resonance is induced in the memristive neuron. Exposed to stronger electromagnetic field, the memristive component can absorb more energy and behave as a signal source for energy shunting, and negative Hamilton energy is obtained for this neuron. The new memristive neuron model can address the main physical properties of biophysical neurons, and it can further be used to explore the collective behaviors and self-organization in networks under energy flow and noisy disturbance.
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Affiliation(s)
- Feifei Yang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Ying Xu
- School of Mathematics and Statistics, Shandong Normal University, Ji'nan 250014, China
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
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12
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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: 7] [Impact Index Per Article: 7.0] [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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13
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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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14
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Li H, Hu J, Chen A, Wang C, Chen L, Tian F, Zhou J, Zhao Y, Chen J, Tong Y, Loh KP, Xu Y, Zhang Y, Hasan T, Yu B. Single-Transistor Neuron with Excitatory-Inhibitory Spatiotemporal Dynamics Applied for Neuronal Oscillations. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2207371. [PMID: 36217845 DOI: 10.1002/adma.202207371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Brain-inspired neuromorphic computing systems with the potential to drive the next wave of artificial intelligence demand a spectrum of critical components beyond simple characteristics. An emerging research trend is to achieve advanced functions with ultracompact neuromorphic devices. In this work, a single-transistor neuron is demonstrated that implements excitatory-inhibitory (E-I) spatiotemporal integration and a series of essential neuron behaviors. Neuronal oscillations, the fundamental mode of neuronal communication, that construct high-dimensional population code to achieve efficient computing in the brain, can also be demonstrated by the neuron transistors. The highly scalable E-I neuron can be the basic building block for implementing core neuronal circuit motifs and large-scale architectural plans to replicate energy-efficient neural computations, forming the foundation of future integrated neuromorphic systems.
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Affiliation(s)
- Hanxi Li
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jiayang Hu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Anzhe Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Chenhao Wang
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Li Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Feng Tian
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Jiachao Zhou
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Yuda Zhao
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jinrui Chen
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Yi Tong
- Technology Development Department, Gusu Laboratory of Materials, Suzhou, 215000, China
| | - Kian Ping Loh
- Department of Chemistry, National University of Singapore, Singapore, 119077, Singapore
| | - Yang Xu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Yishu Zhang
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Tawfique Hasan
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Bin Yu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
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15
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Wang X, Li H. A complementary resistive switching neuron. NANOTECHNOLOGY 2022; 33:355201. [PMID: 35605579 DOI: 10.1088/1361-6528/ac7241] [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] [Received: 02/22/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
The complementary resistive switching (CRS) memristor has originally been proposed for use as the storage element or artificial synapse in large-scale crossbar array with the capability of solving the sneak path problem, but its usage has mainly been hampered by the inherent destructiveness of the read operation (switching '1' state to 'ON' or '0' state). Taking a different perspective on this 'undesired' property, we here report on the inherent behavioral similarity between the CRS memristor and a leaky integrate-and-fire (LIF) neuron which is another basic neural computing element, in addition to synapse. In particular, the mechanism behind the undesired read destructiveness for storage element and artificial synapse can be exploited to naturally realize the LIF and the ensuing spontaneous repolarization processes, followed by a refractory period. By means of this biological similarity, we demonstrate a Pt/Ta2O5-x/TaOy/Ta CRS memristor that can exhibit these neuronal behaviors and perform various fundamental neuronal operations, including additive/subtractive operations and coincidence detection. These results suggest that the CRS neuron, with its bio-interpretability, is a useful addition to the family of memristive neurons.
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Affiliation(s)
- Xinxin Wang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Huanglong Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, People's Republic of China
- Chinese Institute for Brain Research, Beijing 102206, People's Republic of China
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16
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Abbas H, Li J, Ang DS. Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications. MICROMACHINES 2022; 13:mi13050725. [PMID: 35630191 PMCID: PMC9143014 DOI: 10.3390/mi13050725] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/29/2022] [Accepted: 04/29/2022] [Indexed: 11/16/2022]
Abstract
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.
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17
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Deiana AM, Tran N, Agar J, Blott M, Di Guglielmo G, Duarte J, Harris P, Hauck S, Liu M, Neubauer MS, Ngadiuba J, Ogrenci-Memik S, Pierini M, Aarrestad T, Bähr S, Becker J, Berthold AS, Bonventre RJ, Müller Bravo TE, Diefenthaler M, Dong Z, Fritzsche N, Gholami A, Govorkova E, Guo D, Hazelwood KJ, Herwig C, Khan B, Kim S, Klijnsma T, Liu Y, Lo KH, Nguyen T, Pezzullo G, Rasoulinezhad S, Rivera RA, Scholberg K, Selig J, Sen S, Strukov D, Tang W, Thais S, Unger KL, Vilalta R, von Krosigk B, Wang S, Warburton TK. Applications and Techniques for Fast Machine Learning in Science. Front Big Data 2022; 5:787421. [PMID: 35496379 PMCID: PMC9041419 DOI: 10.3389/fdata.2022.787421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/31/2020] [Indexed: 01/10/2023] Open
Abstract
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
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Affiliation(s)
| | - Nhan Tran
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Joshua Agar
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United States
| | | | | | - Javier Duarte
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Philip Harris
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Scott Hauck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Mia Liu
- Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States
| | - Mark S. Neubauer
- Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | | | - Seda Ogrenci-Memik
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Maurizio Pierini
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Thea Aarrestad
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Steffen Bähr
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jürgen Becker
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Anne-Sophie Berthold
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | | | - Tomás E. Müller Bravo
- Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
| | - Markus Diefenthaler
- Thomas Jefferson National Accelerator Facility, Newport News, VA, United States
| | - Zhen Dong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Nick Fritzsche
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | - Amir Gholami
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | | | - Dongning Guo
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | | | - Christian Herwig
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Babar Khan
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Sehoon Kim
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Thomas Klijnsma
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Kin Ho Lo
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Tri Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | - Ryan A. Rivera
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Kate Scholberg
- Department of Physics, Duke University, Durham, NC, United States
| | | | - Sougata Sen
- Birla Institute of Technology and Science, Pilani, India
| | - Dmitri Strukov
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - William Tang
- Department of Physics, Princeton University, Princeton, NJ, United States
| | - Savannah Thais
- Department of Physics, Princeton University, Princeton, NJ, United States
| | | | - Ricardo Vilalta
- Department of Computer Science, University of Houston, Houston, TX, United States
| | - Belina von Krosigk
- Karlsruhe Institute of Technology, Karlsruhe, Germany
- Department of Physics, Universität Hamburg, Hamburg, Germany
| | - Shen Wang
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Thomas K. Warburton
- Department of Physics and Astronomy, Iowa State University, Ames, IA, United States
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Lin J, Liu H, Wang S, Wang D, Wu L. The Image Identification Application with HfO 2-Based Replaceable 1T1R Neural Networks. NANOMATERIALS 2022; 12:nano12071075. [PMID: 35407193 PMCID: PMC9000711 DOI: 10.3390/nano12071075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prepared by connecting the memristor and nMOSFET in series, and a single-layer and a double-layer fully connected neural network are established. The recognition accuracy of 8 × 8 handwritten digital images reaches 95.19%. By randomly replacing the devices with failed devices, it is found that the stuck-off devices have little effect on the accuracy of the network, but the stuck-on devices will cause a sharp reduction of accuracy. By using the measured conductivity adjustment range and precision data of the memristor, the relationship between the recognition accuracy of the network and the number of hidden neurons is simulated. The simulation results match the experimental results. Compared with the neural network based on the precision of 32-bit floating point, the difference is lower than 1%.
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Choi S, Kim GS, Yang J, Cho H, Kang CY, Wang G. Controllable SiO x Nanorod Memristive Neuron for Probabilistic Bayesian Inference. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104598. [PMID: 34618384 DOI: 10.1002/adma.202104598] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010 ) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2 ) and its robustness to the ProbAct variation.
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Affiliation(s)
- Sanghyeon Choi
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Gwang Su Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Jehyeon Yang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Haein Cho
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Chong-Yun Kang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
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20
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Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr 0.7Ca 0.3MnO 3 Memristor. NANOMATERIALS 2021; 11:nano11102684. [PMID: 34685125 PMCID: PMC8538184 DOI: 10.3390/nano11102684] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 11/21/2022]
Abstract
An amorphous Pr0.7Ca0.3MnO3 (PCMO) film was grown on a TiN/SiO2/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.
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21
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Wang J, Teng C, Zhang Z, Chen W, Tan J, Pan Y, Zhang R, Zhou H, Ding B, Cheng HM, Liu B. A Scalable Artificial Neuron Based on Ultrathin Two-Dimensional Titanium Oxide. ACS NANO 2021; 15:15123-15131. [PMID: 34534433 DOI: 10.1021/acsnano.1c05565] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A spiking neural network consists of artificial synapses and neurons and may realize human-level intelligence. Unlike the widely reported artificial synapses, the fabrication of large-scale artificial neurons with good performance is still challenging due to the lack of a suitable material system and integration method. Here, we report an ultrathin (less than10 nm) and inch-size two-dimensional (2D) oxide-based artificial neuron system produced by a controllable assembly of solution-processed 2D monolayer TiOx nanosheets. Artificial neuron devices based on such 2D TiOx films show a high on/off ratio of 109 and a volatile resistance switching phenomenon. The devices can not only emulate the leaky integrate-and-fire activity but also self-recover without additional circuits for sensing and reset. Moreover, the artificial neuron arrays are fabricated and exhibited good uniformity, indicating their large-area integration potential. Our results offer a strategy for fabricating large-scale and ultrathin 2D material-based artificial neurons and 2D spiking neural networks.
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Affiliation(s)
- Jingyun Wang
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Changjiu Teng
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Zhiyuan Zhang
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Wenjun Chen
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Junyang Tan
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Yikun Pan
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Rongjie Zhang
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Heyuan Zhou
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Baofu Ding
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
| | - Hui-Ming Cheng
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China
| | - Bilu Liu
- Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute and Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, People's Republic of China
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Zhang X, Lu J, Wang Z, Wang R, Wei J, Shi T, Dou C, Wu Z, Zhu J, Shang D, Xing G, Chan M, Liu Q, Liu M. Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks. Sci Bull (Beijing) 2021; 66:1624-1633. [PMID: 36654296 DOI: 10.1016/j.scib.2021.04.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/03/2021] [Accepted: 03/26/2021] [Indexed: 02/03/2023]
Abstract
Spiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems.
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Affiliation(s)
- Xumeng Zhang
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jian Lu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
| | - Rui Wang
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinsong Wei
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
| | - Tuo Shi
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Laboratory, Hangzhou 311122, China
| | - Chunmeng Dou
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zuheng Wu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxue Zhu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dashan Shang
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guozhong Xing
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mansun Chan
- Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China
| | - Qi Liu
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Ming Liu
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
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Li ZX, Geng XY, Wang J, Zhuge F. Emerging Artificial Neuron Devices for Probabilistic Computing. Front Neurosci 2021; 15:717947. [PMID: 34421528 PMCID: PMC8377243 DOI: 10.3389/fnins.2021.717947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.
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Affiliation(s)
- Zong-xiao Li
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao-ying Geng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, China
| | - Jingrui Wang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
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Sato H, Shima H, Nokami T, Itoh T, Honma Y, Naitoh Y, Akinaga H, Kinoshita K. Memristors With Controllable Data Volatility by Loading Metal Ion-Added Ionic Liquids. FRONTIERS IN NANOTECHNOLOGY 2021. [DOI: 10.3389/fnano.2021.660563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
We demonstrate a new memristive device (IL-Memristor), in which an ionic liquid (IL) serve as a material to control the volatility of the resistance. ILs are ultra-low vapor pressure liquids consisting of cations and anions at room temperature, and their introduction into solid-state processes can provide new avenues in electronic device fabrication. Because the device resistance change in IL-Memristor is governed by a Cu filament formation/rupture in IL, we considered that the Cu filament stability affects the data retention characteristics. Therefore, we controlled the data retention time by clarifying the corrosion mechanism and performing the IL material design based on the results. It was found out that the corrosion of Cu filaments in the IL was ruled by the comproportionation reaction, and that the data retention characteristics of the devices varied depending on the valence of Cu ions added to the IL. Actually, IL-Memristors involving Cu(II) and Cu(I) show volatile and non-volatile nature with respect to the programmed resistance value, respectively. Our results showed that data volatility can be controlled through the metal ion species added to the IL. The present work indicates that IL-memristor is suitable for unique applications such as artificial neuron with tunable fading characteristics that is applicable to phenomena with a wide range of timescale.
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25
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Wang W, Song W, Yao P, Li Y, Van Nostrand J, Qiu Q, Ielmini D, Yang JJ. Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence. iScience 2020; 23:101809. [PMID: 33305176 PMCID: PMC7718163 DOI: 10.1016/j.isci.2020.101809] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the “non-ideal” behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.
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Affiliation(s)
- Wei Wang
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Wenhao Song
- Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, USA
| | - Peng Yao
- Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, USA
| | - Yang Li
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | | | - Qinru Qiu
- Electrical Engineering and Computer Science Department, Syracuse University, NY, USA
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano 20133, Italy
| | - J Joshua Yang
- Electrical and Computer Engineering Department, University of Southern California, Los Angeles, CA, USA
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26
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Cha JH, Yang SY, Oh J, Choi S, Park S, Jang BC, Ahn W, Choi SY. Conductive-bridging random-access memories for emerging neuromorphic computing. NANOSCALE 2020; 12:14339-14368. [PMID: 32373884 DOI: 10.1039/d0nr01671c] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.
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Affiliation(s)
- Jun-Hwe Cha
- School of Electrical Engineering, Graphene/2D Materials Research Center, Center for Advanced Materials Discovery towards 3D Displays, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
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27
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Wang Y, Zhang Z, Xu M, Yang Y, Ma M, Li H, Pei J, Shi L. Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2019; 11:24230-24240. [PMID: 31119929 DOI: 10.1021/acsami.9b04901] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The accumulation and extrusion of Ca2+ ions in the pre- and post-synaptic terminals play crucial roles in initiating short- and long-term plasticity (STP and LTP) in biological synapses, respectively. Mimicking these synaptic behaviors by electronic devices represents a vital step toward realization of neuromorphic computing. However, the majority of reported synaptic devices usually focus on the emulation of qualitatively synaptic behaviors; devices that can truly emulate the physical behavior of the synaptic Ca2+ ion dynamics in STP and LTP are rarely reported. In this work, Ag/Ag:Ta2O5/Pt self-doping memristors were developed to equivalently emulate the Ca2+ ion dynamics of biological synapses. With conductive filaments from double sources, these memristors produced unique double-switching behavior under voltage sweeps and demonstrated several essential synaptic behaviors under pulse stimuli, including STP, LTP, STP to LTP transition, and spike-rate-dependent plasticity. Experimental results and nanoparticle dynamic simulations both showed that Ag atoms from double sources could mimic Ca2+ dynamics in the pre- and post-synaptic terminals under stimuli. A perceptron network with an STP to LTP transition layer based on the self-doping memristors was also introduced and evaluated; simulations showed that this network could solve noisy figure recognition tasks efficiently. All of these results indicate that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.
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28
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Ji X, Wang C, Lim KG, Tan CC, Chong TC, Zhao R. Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap. ACS APPLIED MATERIALS & INTERFACES 2019; 11:20965-20972. [PMID: 31117430 DOI: 10.1021/acsami.9b02498] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing.
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Affiliation(s)
- Xinglong Ji
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
| | - Chao Wang
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
| | - Kian Guan Lim
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
| | - Chun Chia Tan
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
| | - Tow Chong Chong
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
| | - Rong Zhao
- Department of Engineering Product Design , Singapore University of Technology and Design , 8 Somapah Road , Singapore 487372 , Singapore
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29
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Gao S, Liu G, Yang H, Hu C, Chen Q, Gong G, Xue W, Yi X, Shang J, Li RW. An Oxide Schottky Junction Artificial Optoelectronic Synapse. ACS NANO 2019; 13:2634-2642. [PMID: 30730696 DOI: 10.1021/acsnano.9b00340] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The rapid development of artificial intelligence techniques and future advanced robot systems sparks emergent demand on the accurate perception and understanding of the external environments via visual sensing systems that can co-locate the self-adaptive detecting, processing, and memorizing of optical signals. In this contribution, a simple indium-tin oxide/Nb-doped SrTiO3 (ITO/Nb:SrTiO3) heterojunction artificial optoelectronic synapse is proposed and demonstrated. Through the light and electric field co-modulation of the Schottky barrier profile at the ITO/Nb:SrTiO3 interface, the oxide heterojunction device can respond to the entire visible light region in a neuromorphic manner, allowing synaptic paired-pulse facilitation, short/long-term memory, and "learning-experience" behavior for optical information manipulation. More importantly, the photoplasticity of the artificial synapse has been modulated by heterosynaptic means with a sub-1 V external voltage, not only enabling an optoelectronic analog of the mechanical aperture device showing adaptive and stable optical perception capability under different illuminating conditions but also making the artificial synapse suitable for the mimicry of interest-modulated human visual memories.
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Affiliation(s)
- Shuang Gao
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Gang Liu
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Huali Yang
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Chao Hu
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Qilai Chen
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Guodong Gong
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Wuhong Xue
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Xiaohui Yi
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Jie Shang
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
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