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Mohapatra RAB, Mhaskar CM, Sahu MC, Sahoo S, Roy Chaudhuri A. Neuromorphic learning and recognition in WO 3-xthin film-based forming-free flexible electronic synapses. NANOTECHNOLOGY 2024; 35:455702. [PMID: 39127053 DOI: 10.1088/1361-6528/ad6dce] [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: 06/18/2024] [Accepted: 08/10/2024] [Indexed: 08/12/2024]
Abstract
In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.
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Affiliation(s)
| | | | - Mousam Charan Sahu
- Laboratory for Low Dimensional Materials, Institute of Physics, Bhubaneswar 751005, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India
| | - Satyaprakash Sahoo
- Laboratory for Low Dimensional Materials, Institute of Physics, Bhubaneswar 751005, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India
| | - Ayan Roy Chaudhuri
- Material Science Centre, Indian Institute of Technology, Kharagpur 721302, India
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2
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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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Chee MY, Dananjaya PA, Lim GJ, Du Y, Lew WS. Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiO x-Based Exponential Selector. ACS APPLIED MATERIALS & INTERFACES 2022; 14:35959-35968. [PMID: 35892238 DOI: 10.1021/acsami.2c11016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiOx/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfOx/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O2- and Ti3+ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.
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Affiliation(s)
- Mun Yin Chee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Gerard Joseph Lim
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Yuanmin Du
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
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All-Printed Flexible Memristor with Metal–Non-Metal-Doped TiO2 Nanoparticle Thin Films. NANOMATERIALS 2022; 12:nano12132289. [PMID: 35808124 PMCID: PMC9268177 DOI: 10.3390/nano12132289] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 01/17/2023]
Abstract
A memristor is a fundamental electronic device that operates like a biological synapse and is considered as the solution of classical von Neumann computers. Here, a fully printed and flexible memristor is fabricated by depositing a thin film of metal–non-metal (chromium-nitrogen)-doped titanium dioxide (TiO2). The resulting device exhibited enhanced performance with self-rectifying and forming free bipolar switching behavior. Doping was performed to bring stability in the performance of the memristor by controlling the defects and impurity levels. The forming free memristor exhibited characteristic behavior of bipolar resistive switching with a high on/off ratio (2.5 × 103), high endurance (500 cycles), long retention time (5 × 103 s) and low operating voltage (±1 V). Doping the thin film of TiO2 with metal–non-metal had a significant effect on the switching properties and conduction mechanism as it directly affected the energy bandgap by lowering it from 3.2 eV to 2.76 eV. Doping enhanced the mobility of charge carriers and eased the process of filament formation by suppressing its randomness between electrodes under the applied electric field. Furthermore, metal–non-metal-doped TiO2 thin film exhibited less switching current and improved non-linearity by controlling the surface defects.
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Giotis C, Serb A, Manouras V, Stathopoulos S, Prodromakis T. Palimpsest memories stored in memristive synapses. SCIENCE ADVANCES 2022; 8:eabn7920. [PMID: 35731877 PMCID: PMC9217086 DOI: 10.1126/sciadv.abn7920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Biological synapses store multiple memories on top of each other in a palimpsest fashion and at different time scales. Palimpsest consolidation is facilitated by the interaction of hidden biochemical processes governing synaptic efficacy during varying lifetimes. This arrangement allows idle memories to be temporarily overwritten without being forgotten, while previously unseen memories are used in the short term. While embedded artificial intelligence can greatly benefit from this functionality, a practical demonstration in hardware is missing. Here, we show how the intrinsic properties of metal-oxide volatile memristors emulate the processes supporting biological palimpsest consolidation. Our memristive synapses exhibit an expanded doubled capacity and protect a consolidated memory while up to hundreds of uncorrelated short-term memories temporarily overwrite it, without requiring specialized instructions. We further demonstrate this technology in the context of visual working memory. This showcases how emerging memory technologies can efficiently expand the capabilities of artificial intelligence hardware toward more generalized learning memories.
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Affiliation(s)
- Christos Giotis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Alexander Serb
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
| | - Vasileios Manouras
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Spyros Stathopoulos
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Themis Prodromakis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
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6
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Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing. Nat Commun 2022; 13:2074. [PMID: 35440122 PMCID: PMC9018677 DOI: 10.1038/s41467-022-29727-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/28/2022] [Indexed: 12/18/2022] Open
Abstract
Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. “Reconfigurable memristors” that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes – volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices. Existing memristors cannot be reconfigured to meet the diverse switching requirements of various computing frameworks, limiting their universality. Here, the authors present a nanocrystal memristor that can be reconfigured on-demand to address these limitations
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Fang X, Duan S, Wang L. Memristive Hodgkin-Huxley Spiking Neuron Model for Reproducing Neuron Behaviors. Front Neurosci 2021; 15:730566. [PMID: 34630019 PMCID: PMC8496503 DOI: 10.3389/fnins.2021.730566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/16/2021] [Indexed: 11/18/2022] Open
Abstract
The Hodgkin-Huxley (HH) spiking neuron model reproduces the dynamic characteristics of the neuron by mimicking the action potential, ionic channels, and spiking behaviors. The memristor is a nonlinear device with variable resistance. In this paper, the memristor is introduced to the HH spiking model, and the memristive Hodgkin-Huxley spiking neuron model (MHH) is presented. We experimentally compare the HH spiking model and the MHH spiking model by applying different stimuli. First, the individual current pulse is injected into the HH and MHH spiking models. The comparison between action potentials, current densities, and conductances is carried out. Second, the reverse single pulse stimulus and a series of pulse stimuli are applied to the two models. The effects of current density and action time on the production of the action potential are analyzed. Finally, the sinusoidal current stimulus acts on the two models. The various spiking behaviors are realized by adjusting the frequency of the sinusoidal stimulus. We experimentally demonstrate that the MHH spiking model generates more action potential than the HH spiking model and takes a short time to change the memductance. The reverse stimulus cannot activate the action potential in both models. The MHH spiking model performs smoother waveforms and a faster speed to return to the resting potential. The larger the external stimulus, the faster action potential generated, and the more noticeable change in conductances. Meanwhile, the MHH spiking model shows the various spiking patterns of neurons.
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Affiliation(s)
- Xiaoyan Fang
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Shukai Duan
- College of Artificial Intelligence, Southwest University, Chongqing, China.,Brain-Inspired Computing and Intelligent Control of Chongqing Key Lab, Chongqing, China.,National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing, China.,Chongqing Brain Science Collaborative Innovation Center, Chongqing, China
| | - Lidan Wang
- School of Electronic and Information Engineering, Southwest University, Chongqing, China.,Brain-Inspired Computing and Intelligent Control of Chongqing Key Lab, Chongqing, China.,National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing, China.,Chongqing Brain Science Collaborative Innovation Center, Chongqing, China
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8
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Xue F, He X, Wang Z, Retamal JRD, Chai Z, Jing L, Zhang C, Fang H, Chai Y, Jiang T, Zhang W, Alshareef HN, Ji Z, Li LJ, He JH, Zhang X. Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low-Power Neuromorphic In-Memory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2008709. [PMID: 33860581 DOI: 10.1002/adma.202008709] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/15/2021] [Indexed: 06/12/2023]
Abstract
Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In2 Se3 can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 103 , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In2 Se3 channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In2 Se3 memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In2 Se3 neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.
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Affiliation(s)
- Fei Xue
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Xin He
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Zhenyu Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - José Ramón Durán Retamal
- Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Zheng Chai
- Department of Electronics and Electrical Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Lingling Jing
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chenhui Zhang
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Hui Fang
- Computer Science Department, Loughborough University, Loughborough, LE11 3TU, UK
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Tao Jiang
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
| | - Weidong Zhang
- Department of Electronics and Electrical Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Husam N Alshareef
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Zhigang Ji
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Lain-Jong Li
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- Department of Materials Science and Engineering, University of New South Wales, Kensington, NSW, 2052, Australia
| | - Jr-Hau He
- Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Xixiang Zhang
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
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Shen Z, Zhao C, Qi Y, Xu W, Liu Y, Mitrovic IZ, Yang L, Zhao C. Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E1437. [PMID: 32717952 PMCID: PMC7466260 DOI: 10.3390/nano10081437] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/24/2022]
Abstract
Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.
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Affiliation(s)
- Zongjie Shen
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Chun Zhao
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Yanfei Qi
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710061, China
| | - Wangying Xu
- College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Yina Liu
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
| | - Ivona Z. Mitrovic
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Li Yang
- Department of Chemistry, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
| | - Cezhou Zhao
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
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Chen X, Suen CH, Yau HM, Zhou F, Chai Y, Tang X, Zhou X, Onofrio N, Dai JY. A dual mode electronic synapse based on layered SnSe films fabricated by pulsed laser deposition. NANOSCALE ADVANCES 2020; 2:1152-1160. [PMID: 36133057 PMCID: PMC9418994 DOI: 10.1039/c9na00447e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 01/16/2020] [Indexed: 06/13/2023]
Abstract
An artificial synapse, such as a memristive electronic synapse, has caught world-wide attention due to its potential in neuromorphic computing, which may tremendously reduce computer volume and energy consumption. The introduction of layered two-dimensional materials has been reported to enhance the performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimensional films by scalable methods, which has greatly limited the industrial application potential of two-dimensional materials. In this work, a scalable pulsed laser deposition (PLD) method has been utilized to fabricate large-area layered SnSe films, which are used as the functional layers of the memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been achieved in this SnSe-based memristive electronic synapse. The switching between Mode 1 and Mode 2 can be realized by a series of voltage sweeping and programmed pulses. The formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation can be the origin of long-term memristive behaviour. DFT calculations were performed to further illustrate how Ag atoms and Sn vacancies diffuse through the SnSe layer and form filaments. The successful emulation of synaptic functions by the layered chalcogenide memristor fabricated by the PLD method suggests the application potential in future neuromorphic computers.
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Affiliation(s)
- Xinxin Chen
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Chun-Hung Suen
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Hei-Man Yau
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Feichi Zhou
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Xiaodan Tang
- College of Physics, Chongqing University Chongqing 401331 P. R. China
| | - Xiaoyuan Zhou
- College of Physics, Chongqing University Chongqing 401331 P. R. China
| | - Nicolas Onofrio
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
| | - Ji-Yan Dai
- Department of Applied Physics, The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong
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11
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Serb A, Corna A, George R, Khiat A, Rocchi F, Reato M, Maschietto M, Mayr C, Indiveri G, Vassanelli S, Prodromakis T. Memristive synapses connect brain and silicon spiking neurons. Sci Rep 2020; 10:2590. [PMID: 32098971 PMCID: PMC7042282 DOI: 10.1038/s41598-020-58831-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/21/2020] [Indexed: 11/09/2022] Open
Abstract
Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.
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Affiliation(s)
- Alexantrou Serb
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrea Corna
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Richard George
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Ali Khiat
- Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
| | - Federico Rocchi
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marco Reato
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Marta Maschietto
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Christian Mayr
- Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - Stefano Vassanelli
- Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy.
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Bao H, Hu A, Liu W, Bao B. Hidden Bursting Firings and Bifurcation Mechanisms in Memristive Neuron Model With Threshold Electromagnetic Induction. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:502-511. [PMID: 30990198 DOI: 10.1109/tnnls.2019.2905137] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Memristors can be employed to mimic biological neural synapses or to describe electromagnetic induction effects. To exhibit the threshold effect of electromagnetic induction, this paper presents a threshold flux-controlled memristor and examines its frequency-dependent pinched hysteresis loops. Using an electromagnetic induction current generated by the threshold memristor to replace the external current in 2-D Hindmarsh-Rose (HR) neuron model, a 3-D memristive HR (mHR) neuron model with global hidden oscillations is established and the corresponding numerical simulations are performed. It is found that due to no equilibrium point, the obtained mHR neuron model always operates in hidden bursting firing patterns, including coexisting hidden bursting firing patterns with bistability also. In addition, the model exhibits complex dynamics of the actual neuron electrical activities, which acts like the 3-D HR neuron model, indicating its feasibility. In particular, by constructing the fold and Hopf bifurcation sets of the fast-scale subsystem, the bifurcation mechanisms of hidden bursting firings are expounded. Finally, circuit experiments on hardware breadboards are deployed and the captured results well match with the numerical results, validating the physical mechanism of biological neuron and the reliability of electronic neuron.
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Park SM, Hwang HG, Woo JU, Lee WH, Chae SJ, Nahm S. Improvement of Conductance Modulation Linearity in a Cu 2+-Doped KNbO 3 Memristor through the Increase of the Number of Oxygen Vacancies. ACS APPLIED MATERIALS & INTERFACES 2020. [PMID: 31820625 DOI: 10.1016/j.apmt.2020.100582] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The Pt/KNbO3/TiN/Si (KN) memristor exhibits various biological synaptic properties. However, it also displays nonlinear conductance modulation with the application of identical pulses, indicating that it should be improved for neuromorphic applications. The abrupt change of the conductance originates from the inhomogeneous growth/dissolution of oxygen vacancy filaments in the KN film. The change of the filaments in a KN film is controlled by two mechanisms with different growth/dissolution rates: a redox process with a fast rate and an oxygen vacancy diffusion process with a slow rate. Therefore, the conductance modulation linearity can be improved if the growth/dissolution of the filaments is controlled by only one mechanism. When the number of oxygen vacancies in the KN film was increased through doping of Cu2+ ions, the growth/dissolution of the filaments in the Cu2+-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.
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14
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Kang D, Kim J, Oh S, Park H, Dugasani SR, Kang B, Choi C, Choi R, Lee S, Park SH, Heo K, Park J. A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networks. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2019; 6:1901265. [PMID: 31508292 PMCID: PMC6724472 DOI: 10.1002/advs.201901265] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Indexed: 05/24/2023]
Abstract
A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+-doped salmon deoxyribonucleic acid (S-DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S-DNA electrolyte, the synaptic operation of the S-DNA device features special long-term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrate-and-fire," is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S-DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (|αp|: 31→20, |αd|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single-layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S-DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
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Affiliation(s)
- Dong‐Ho Kang
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
- School of Electrical and Electronic EngineeringNanyang Technological University50 Nanyang Avenue639798SingaporeSingapore
| | - Jeong‐Hoon Kim
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Seyong Oh
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Hyung‐Youl Park
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | | | - Beom‐Seok Kang
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Changhwan Choi
- Division of Materials Science and EngineeringHanyang UniversitySeoul133–791South Korea
| | - Rino Choi
- Material Science and EngineeringInha UniversityIncheon402–751South Korea
| | - Sungjoo Lee
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440–746South Korea
| | - Sung Ha Park
- Department of PhysicsSungkyunkwan UniversitySuwon440‐746South Korea
| | - Keun Heo
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Jin‐Hong Park
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440–746South Korea
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Abstract
We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.
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Zhang Y, He W, Wu Y, Huang K, Shen Y, Su J, Wang Y, Zhang Z, Ji X, Li G, Zhang H, Song S, Li H, Sun L, Zhao R, Shi L. Highly Compact Artificial Memristive Neuron with Low Energy Consumption. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2018; 14:e1802188. [PMID: 30427578 DOI: 10.1002/smll.201802188] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/22/2018] [Indexed: 06/09/2023]
Abstract
Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.
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Affiliation(s)
- Yishu Zhang
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore
| | - Wei He
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yujie Wu
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Kejie Huang
- College of Information Science and Electronic Engineering, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China
| | - Yangshu Shen
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore
| | - Jiasheng Su
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore
| | - Yaoyuan Wang
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Ziyang Zhang
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Xinglong Ji
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore
| | - Guoqi Li
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Hongtao Zhang
- SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Sen Song
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Huanglong Li
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Litao Sun
- SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Rong Zhao
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore
| | - Luping Shi
- Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
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17
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Raeber TJ, Barlow AJ, Zhao ZC, McKenzie DR, Partridge JG, McCulloch DG, Murdoch BJ. Sensory gating in bilayer amorphous carbon memristors. NANOSCALE 2018; 10:20272-20278. [PMID: 30362489 DOI: 10.1039/c8nr05328f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Multi-state amorphous carbon-based memory devices have been developed that exhibit both bipolar and unipolar resistive switching behaviour. These modes of operation were implemented independently to access multiple resistance states, enabling higher memory density than conventional binary non-volatile memory technologies. The switching characteristics have been further utilised to study synaptic computational functions that could be implemented in artificial neural networks. Notably, paired-pulse inhibition (PPI) is observed at bio-realistic timescales (<100 ms). Devices displaying this rich synaptic behaviour could function as robust stand-alone synapse-inspired memory or be applied as filters for specialised neuromorphic circuits and sensors.
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Affiliation(s)
- T J Raeber
- School of Science, RMIT University, VIC 3001, Melbourne, Australia.
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18
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Chakraborty I, Roy D, Roy K. Technology Aware Training in Memristive Neuromorphic Systems for Nonideal Synaptic Crossbars. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2018. [DOI: 10.1109/tetci.2018.2829919] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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19
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The Role of Short-Term Plasticity in Neuromorphic Learning: Learning from the Timing of Rate-Varying Events with Fatiguing Spike-Timing-Dependent Plasticity. IEEE NANOTECHNOLOGY MAGAZINE 2018. [DOI: 10.1109/mnano.2018.2845479] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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20
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Osipov V, Osipova M. Space–time signal binding in recurrent neural networks with controlled elements. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.05.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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21
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Gupta I, Serb A, Khiat A, Zeitler R, Vassanelli S, Prodromakis T. Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:351-359. [PMID: 29570062 DOI: 10.1109/tbcas.2018.2797939] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.
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22
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Yin XB, Yang R, Xue KH, Tan ZH, Zhang XD, Miao XS, Guo X. Mimicking the brain functions of learning, forgetting and explicit/implicit memories with SrTiO 3-based memristive devices. Phys Chem Chem Phys 2018; 18:31796-31802. [PMID: 27841389 DOI: 10.1039/c6cp06049h] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO3/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the "time-saving" effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO3 interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.
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Affiliation(s)
- Xue-Bing Yin
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.
| | - Rui Yang
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.
| | - Kan-Hao Xue
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Zheng-Hua Tan
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.
| | - Xiao-Dong Zhang
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.
| | - Xiang-Shui Miao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Xin Guo
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.
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23
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Ignatov M, Ziegler M, Hansen M, Kohlstedt H. Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion. SCIENCE ADVANCES 2017; 3:e1700849. [PMID: 29075665 PMCID: PMC5656427 DOI: 10.1126/sciadv.1700849] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 09/26/2017] [Indexed: 05/15/2023]
Abstract
The human brain is able to integrate a myriad of information in an enormous and massively parallel network of neurons that are divided into functionally specialized regions such as the visual cortex, auditory cortex, or dorsolateral prefrontal cortex. Each of these regions participates as a context-dependent, self-organized, and transient subnetwork, which is shifted by changes in attention every 0.5 to 2 s. This leads to one of the most puzzling issues in cognitive neuroscience, well known as the "binding problem." The concept of neural synchronization tries to explain the problem by encoding information using coherent states, which temporally patterns neural activity. We show that memristive devices, that is, a two-terminal variable resistor that changes its resistance depending on the previous charge flow, allow a new degree of freedom for this concept: a local memory that supports transient connectivity patterns in oscillator networks. On the basis of the probability distribution of the resistance switching process of Ag-doped titanium dioxide memristive devices, a local plasticity model is proposed, which causes an autonomous phase and frequency locking in an oscillator network. To illustrate the performance of the proposed computing paradigm, the temporal binding problem is investigated in a network of memristively coupled self-sustained van der Pol oscillators. We show evidence that the implemented network allows achievement of the transition from asynchronous to multiple synchronous states, which opens a new pathway toward the construction of cognitive electronics.
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Affiliation(s)
- Marina Ignatov
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, Kiel 24143, Germany
| | - Martin Ziegler
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, Kiel 24143, Germany
| | - Mirko Hansen
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, Kiel 24143, Germany
| | - Hermann Kohlstedt
- Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, Kiel 24143, Germany
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24
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Yang CS, Shang DS, Liu N, Shi G, Shen X, Yu RC, Li YQ, Sun Y. A Synaptic Transistor based on Quasi-2D Molybdenum Oxide. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2017; 29. [PMID: 28485032 DOI: 10.1002/adma.201700906] [Citation(s) in RCA: 139] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 03/24/2017] [Indexed: 05/08/2023]
Abstract
Biological synapses store and process information simultaneously by tuning the connection between two neighboring neurons. Such functionality inspires the task of hardware implementation of neuromorphic computing systems. Ionic/electronic hybrid three-terminal memristive devices, in which the channel conductance can be modulated according to the history of applied voltage and current, provide a more promising way of emulating synapses by a substantial reduction in complexity and energy consumption. 2D van der Waals materials with single or few layers of crystal unit cells have been a widespread innovation in three-terminal electronic devices. However, less attention has been paid to 2D transition-metal oxides, which have good stability and technique compatibility. Here, nanoscale three-terminal memristive transistors based on quasi-2D α-phase molybdenum oxide (α-MoO3 ) to emulate biological synapses are presented. The essential synaptic behaviors, such as excitatory postsynaptic current, depression and potentiation of synaptic weight, and paired-pulse facilitation, as well as the transition of short-term plasticity to long-term potentiation, are demonstrated in the three-terminal devices. These results provide an insight into the potential application of 2D transition-metal oxides for synaptic devices with high scaling ability, low energy consumption, and high processing efficiency.
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Affiliation(s)
- Chuan Sen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Da Shan Shang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Nan Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Gang Shi
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Xi Shen
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Ri Cheng Yu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Yong Qing Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
| | - Young Sun
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, P. R. China
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25
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Niu G, Schubert MA, Sharath SU, Zaumseil P, Vogel S, Wenger C, Hildebrandt E, Bhupathi S, Perez E, Alff L, Lehmann M, Schroeder T, Niermann T. Electron holography on HfO 2/HfO 2-x bilayer structures with multilevel resistive switching properties. NANOTECHNOLOGY 2017; 28:215702. [PMID: 28462907 DOI: 10.1088/1361-6528/aa6cd9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Unveiling the physical nature of the oxygen-deficient conductive filaments (CFs) that are responsible for the resistive switching of the HfO2-based resistive random access memory (RRAM) devices represents a challenging task due to the oxygen vacancy related defect nature and nanometer size of the CFs. As a first important step to this goal, we demonstrate in this work direct visualization and a study of physico-chemical properties of oxygen-deficient amorphous HfO2-x by carrying out transmission electron microscopy electron holography as well as energy dispersive x-ray spectroscopy on HfO2/HfO2-x bilayer heterostructures, which are realized by reactive molecular beam epitaxy. Furthermore, compared to single layer devices, Pt/HfO2/HfO2-x /TiN bilayer devices show enhanced resistive switching characteristics with multilevel behavior, indicating their potential as electronic synapses in future neuromorphic computing applications.
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Affiliation(s)
- G Niu
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China. IHP, Im Technologiepark 25, D-15236 Frankfurt (Oder), Germany
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26
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Khiat A, Cortese S, Serb A, Prodromakis T. Resistive switching of Pt/TiO x /Pt devices fabricated on flexible Parylene-C substrates. NANOTECHNOLOGY 2017; 28:025303. [PMID: 27924782 DOI: 10.1088/1361-6528/28/2/025303] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Pt/TiO x /Pt resistive switching (RS) devices are considered to be amongst the most promising candidates in memristor family and the technology transfer to flexible substrates could open the way to new opportunities for flexible memory implementations. Hence, an important goal is to achieve a fully flexible RS memory technology. Nonetheless, several fabrication challenges are present and must be solved prior to achieving reliable device fabrication and good electronic performances. Here, we propose a fabrication method for the successful transfer of Pt/TiO x /Pt stack onto flexible Parylene-C substrates. The devices were electrically characterised, exhibiting both digital and analogue memory characteristics, which are obtained by proper adjustment of pulsing schemes during tests. This approach could open new application possibilities of these devices in neuromorphic computing, data processing, implantable sensors and bio-compatible neural interfaces.
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Affiliation(s)
- Ali Khiat
- Nanoelectronics and Nanotechnology Research Group, Department of Electronics and Computer Science, University of Southampton, University Road, SO17 1BJ, Southampton, UK. Southampton Nanofabrication Centre, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
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27
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Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat Commun 2016; 7:12611. [PMID: 27681181 PMCID: PMC5056401 DOI: 10.1038/ncomms12611] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022] Open
Abstract
In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.
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Affiliation(s)
- Alexander Serb
- Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK
| | - Johannes Bill
- Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
- Heidelberg University, Department of Physics and Astronomy, Kirchhoff Institute for Physics, 69120 Heidelberg, Germany
| | - Ali Khiat
- Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK
| | - Radu Berdan
- Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK
| | - Robert Legenstein
- Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
| | - Themis Prodromakis
- Electronics and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK
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28
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Wan CJ, Liu YH, Feng P, Wang W, Zhu LQ, Liu ZP, Shi Y, Wan Q. Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2016; 28:5878-5885. [PMID: 27159546 DOI: 10.1002/adma.201600820] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/11/2016] [Indexed: 06/05/2023]
Abstract
Flexible metal oxide/graphene oxide hybrid multi-gate neuromorphic transistors are fabricated on flexible conducting graphene substrates. Dendritic integrations in both spatial and temporal modes are emulated, and spatiotemporal correlated logics are obtained. A proof-of-principle visual system model for emulating Lobula Giant Motion Detector neuron is also investigated. The results are of great significance for flexible sensors and neuromorphic cognitive systems.
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Affiliation(s)
- Chang Jin Wan
- School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Yang Hui Liu
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Ping Feng
- School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Wei Wang
- School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Li Qiang Zhu
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Zhao Ping Liu
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Yi Shi
- School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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Gkoupidenis P, Koutsouras DA, Lonjaret T, Fairfield JA, Malliaras GG. Orientation selectivity in a multi-gated organic electrochemical transistor. Sci Rep 2016; 6:27007. [PMID: 27245574 PMCID: PMC4887893 DOI: 10.1038/srep27007] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 05/12/2016] [Indexed: 11/23/2022] Open
Abstract
Neuromorphic devices offer promising computational paradigms that transcend the limitations of conventional technologies. A prominent example, inspired by the workings of the brain, is spatiotemporal information processing. Here we demonstrate orientation selectivity, a spatiotemporal processing function of the visual cortex, using a poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) organic electrochemical transistor with multiple gates. Spatially distributed inputs on a gate electrode array are found to correlate with the output of the transistor, leading to the ability to discriminate between different stimuli orientations. The demonstration of spatiotemporal processing in an organic electronic device paves the way for neuromorphic devices with new form factors and a facile interface with biology.
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Affiliation(s)
- Paschalis Gkoupidenis
- Department of Bioelectronics, Ecole Nationale Supérieure des Mines, CMP-EMSE, MOC, 13541 Gardanne, France
| | - Dimitrios A Koutsouras
- Department of Bioelectronics, Ecole Nationale Supérieure des Mines, CMP-EMSE, MOC, 13541 Gardanne, France
| | - Thomas Lonjaret
- Department of Bioelectronics, Ecole Nationale Supérieure des Mines, CMP-EMSE, MOC, 13541 Gardanne, France.,MicroVitae Technologies, Hôtel Technologique, Europarc Sainte Victoire Bât 6, Route de Valbrillant, 13590 Meyreuil, France
| | - Jessamyn A Fairfield
- School of Chemistry and CRANN Institute, Trinity College Dublin, Dublin 2, Ireland
| | - George G Malliaras
- Department of Bioelectronics, Ecole Nationale Supérieure des Mines, CMP-EMSE, MOC, 13541 Gardanne, France
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Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film. Sci Rep 2016; 6:18915. [PMID: 26739613 PMCID: PMC4703968 DOI: 10.1038/srep18915] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Accepted: 11/30/2015] [Indexed: 11/08/2022] Open
Abstract
The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.
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