A Comprehension Survey on the Characterization and Development of Neuromorphic Devices

CAO Zhen; ZHANG Yu-xuan; LI Ling-lei; GUO Zhang; SUN Qi; CAO Rong-rong; HOU Biao; JIAO Li-cheng

doi:10.12263/DZXB.20230458

您当前的位置：

首页 >

文章列表页 >

A Comprehension Survey on the Characterization and Development of Neuromorphic Devices

SURVEYS AND REVIEWS | 更新时间：2025-12-11

- A Comprehension Survey on the Characterization and Development of Neuromorphic Devices
- ACTA ELECTRONICA SINICA Vol. 51, Issue 12, Pages: 3619-3642(2023)
- 作者机构：
  
  1.西安电子科技大学人工智能学院,陕西西安 710126
  2.西安电子科技大学杭州研究院,浙江杭州 311231
  3.国防科技大学电子科学学院,湖南长沙 410073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62104176;62171347;62004220;62301395);China Postdoctoral Science Foundation Project(2019M663637;2020M673696);Proof-of-concept Fund Project of Hangzhou Research Institute of Xidian University(GNYZ2023XJ0409-1);Basic Research Program of Natural Sciences of Shaanxi Province(2022JQ-661);Special Fund Project for Basic Scientific Research Business Funds of Central Universities(XJSJ23083;XJS222215)
- DOI：10.12263/DZXB.20230458
  CLC： TP183;
- Received：25 May 2023，
  
  Revised：2023-10-08，
  
  Published：25 December 2023
- 稿件说明：
移动端阅览
曹震,张浴轩,李灵蕾等.神经形态器件的特性与发展[J].电子学报,2023,51(12):3619-3642.

CAO Zhen,ZHANG Yu-xuan,LI Ling-lei,et al.A Comprehension Survey on the Characterization and Development of Neuromorphic Devices[J].ACTA ELECTRONICA SINICA,2023,51(12):3619-3642.
曹震,张浴轩,李灵蕾等.神经形态器件的特性与发展[J].电子学报,2023,51(12):3619-3642. DOI： 10.12263/DZXB.20230458.

CAO Zhen,ZHANG Yu-xuan,LI Ling-lei,et al.A Comprehension Survey on the Characterization and Development of Neuromorphic Devices[J].ACTA ELECTRONICA SINICA,2023,51(12):3619-3642. DOI： 10.12263/DZXB.20230458.

摘要

随着大数据时代的到来和人类对人脑系统研究的日渐深入，类脑计算领域的研究取得突破性进展，有希望在根源上打破传统计算机的性能瓶颈.神经突触是对人体脑部进行记忆能力训练和处理数据的重要基础单元，因此开发新材料、新结构，研究基于新型人造材料与光电子器件的神经突触可塑性，对神经形态器件研究和类脑硬件设计的实现都有着重要意义.本文首先指出目前“冯·诺依曼架构”的主要性能瓶颈，引出类脑计算的概念，提出神经形态器件的主要性能优势，并梳理神经形态器件发展历史；然后在忆阻器领域，阐述与分析忆阻类型、忆阻结构与忆阻机理，比较出几种忆阻器的特性，举例说明忆阻器在不同领域的应用；接着以神经形态器件为基础，选取磁性隧道结、新型浮栅管和铁电晶体管，介绍其结构、工作原理与应用；最后总结目前神经形态器件发展的成果和方向，并对行业发展前景进行预测.

Abstract

With the advent of the big data era and the increasingly in-depth study of the human brain system

the field of neuromorphic computing has made breakthrough progress

offering hope to break through the performance issue of traditional computers at the root level. Neural synapses are important basic units for memory training and data processing in the human brain

therefore

it is of great significance for research on neural morphological devices and the implementation of neuromorphic hardware design to develop new materials and structures to study the plasticity of neural synapses based on novel artificial materials and optoelectronic devices. This paper firstly points out the main performance issue of Von Neumann architecture

draws forth the concept of brain-like computing

puts forward the main performance advantages of neuromorphic devices

and sorts out the development history of neuromorphic devices. Then in the field of memristors

the types of memristors

memristor structures and memristor mechanisms are described and analyzed

the advantages and disadvantages of several types of memristors are compared

and the examples of applications of memristors in different fields are presented. Next

based on neural morphological devices

the structures

working principles and applications of magnetic tunnel junctions

new floating gate transistors

and ferroelectric transistors are selected to introduce. Finally

this paper summarizes the achievements and directions of the current development of neural morphological devices and predicts the development prospects of the industry.

关键词

Keywords

references

邵博文 . 冯·诺依曼的逻辑和计算机思想 [J ] . 数字通信世界 , 2017 ( 10 ): 249 .

SHAO B W . Von Neumann's logic and computer thought [J ] . Digital Communication World , 2017 ( 10 ): 249 . (in Chinese)

龚庆悦 , 董海艳 , 冒宇清 . 医学信息工程概论 [M ] . 南京 : 南京大学出版社 , 2019 .

GONG Q Y , DONG H Y , MAO Y Q . Introduction to Medical Information Engineering [M ] . Nanjing : Nanjing University Press , 2019 . (in Chinese)

MARKRAM H . The Human Brain Project A Report to the European Commission [R ] . The HBP Report , 2012 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

TENENBAUM J B , KEMP C , GRIFFITHS T L , et al . How to grow a mind: Statistics, structure, and abstraction [J ] . Science , 2011 , 331 ( 6022 ): 1279 - 1285 .

殷明慧 , 杨玉超 , 黄如 . 神经形态器件现状与未来 [J ] . 国防科技 , 2016 , 37 ( 6 ): 23 - 30 .

YIN M H , YANG Y C , HUANG R . Progress and outlook of neuromorphic devices [J ] . National Defense Science & Technology , 2016 , 37 ( 6 ): 23 - 30 . (in Chinese)

MEAD C . Neuromorphic electronic systems [J ] . Proceedings of the IEEE , 1990 , 78 ( 10 ): 1629 - 1636 .

SHIBATA T , OHMI T . A functional MOS transistor featuring gate-level weighted sum and threshold operations [J ] . IEEE Transactions on Electron Devices , 1992 , 39 ( 6 ): 1444 - 1455 .

SEO J S , BREZZO B , LIU Y , et al . A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons [C ] // 2011 IEEE Custom Integrated Circuits Conference (CICC) . Piscataway : IEEE , 2011 : 1 - 4 .

MEROLLA P A , ARTHUR J V , ALVAREZ-ICAZA R , et al . Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface [J ] . Science , 2014 , 345 ( 6197 ): 668 - 673 .

BENJAMIN B V , GAO P R , MCQUINN E , et al . Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations [J ] . Proceedings of the IEEE , 2014 , 102 ( 5 ): 699 - 716 .

DAVIES M , SRINIVASA N , LIN T H , et al . Loihi: A neuromorphic manycore processor with on-chip learning [J ] . IEEE Micro , 2018 , 38 ( 1 ): 82 - 99 .

PEI J , DENG L , SONG S , et al . Towards artificial general intelligence with hybrid Tianjic chip architecture [J ] . Nature , 2019 , 572 ( 7767 ): 106 - 111 .

张玲 , 刘国柱 , 于宗光 . 人工神经形态器件发展现状与展望 [J ] . 电子与封装 , 2021 , 21 ( 6 ): 5 - 18 .

ZHANG L , LIU G Z , YU Z G . Recent advances in neuromorphic devices [J ] . Electronics & Packaging , 2021 , 21 ( 6 ): 5 - 18 . (in Chinese)

谢生 , 高旭斌 , 毛陆虹 . 基于标准CMOS工艺的光电浮栅突触研究 [J ] . 天津大学学报(自然科学与工程技术版) , 2022 , 55 ( 7 ): 701 - 705 .

XIE S , GAO X B , MAO L H . Photoelectric floating-gate synapse based on standard CMOS process [J ] . Journal of Tianjin University (Science and Technology) , 2022 , 55 ( 7 ): 701 - 705 . (in Chinese)

CHUA L . Memristor-The missing circuit element [J ] . IEEE Transactions on Circuit Theory , 1971 , 18 ( 5 ): 507 - 519 .

STRUKOV D B , SNIDER G S , STEWART D R , et al . The missing memristor found [J ] . Nature , 2008 , 453 ( 7191 ): 80 - 83 .

JO S H , CHANG T , EBONG I , et al . Nanoscale memristor device as synapse in neuromorphic systems [J ] . Nano Letters , 2010 , 10 ( 4 ): 1297 - 1301 .

KIM S , DU C , SHERIDAN P , et al . Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity [J ] . Nano Letters , 2015 , 15 ( 3 ): 2203 - 2211 .

WANG Z W , YIN M H , ZHANG T , et al . Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing [J ] . Nanoscale , 2016 , 8 ( 29 ): 14015 - 14022 .

WANG Z R , JOSHI S , SAVEL’EV S E , et al . Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing [J ] . Nature Materials , 2017 , 16 ( 1 ): 101 - 108 .

LV Z Y , WANG Y , CHEN J R , et al . Semiconductor quantum dots for memories and neuromorphic computing systems [J ] . Chemical Reviews , 2020 , 120 ( 9 ): 3941 - 4006 .

JI X L , ZHAO X Y , TAN M C , et al . Artificial perception built on memristive system: Visual, auditory, and tactile sensations [J ] . Advanced Intelligent Systems , 2020 , 2 ( 3 ): 1900118 .

LIU K Q , ZHANG T , DANG B J , et al . An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing [J ] . Nature Electronics , 2022 , 5 ( 11 ): 761 - 773 .

朱佳雪 , 张续猛 , 王睿 , 等 . 面向神经形态感知和计算的柔性忆阻器基脉冲神经元 [J ] . 物理学报 , 2022 , 71 ( 14 ): 338 - 349 .

ZHU J X , ZHANG X M , WANG R , et al . Flexible memristive spiking neuron for neuromorphic sensing and computing [J ] . Acta Physica Sinica , 2022 , 71 ( 14 ): 338 - 349 . (in Chinese)

OVSHINSKY S R . Reversible electrical switching phenomena in disordered structures [J ] . Physical Review Letters , 1968 , 21 ( 20 ): 1450 - 1453 .

JULLIERE M . Tunneling between ferromagnetic films [J ] . Physics Letters A , 1975 , 54 ( 3 ): 225 - 226 .

SURI M N , BICHLER O , QUERLIOZ D , et al . Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction [C ] // 2011 International Electron Devices Meeting . Piscataway : IEEE , 2012 : 4.4.1- 4 . 4 . 4 .

KUZUM D , JEYASINGH R G D , LEE B , et al . Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing [J ] . Nano Letters , 2012 , 12 ( 5 ): 2179 - 2186 .

TUMA T , PANTAZI A , LE GALLO M , et al . Stochastic phase-change neurons [J ] . Nature Nanotechnology , 2016 , 11 ( 8 ): 693 - 699 .

SRINIVASAN G , SENGUPTA A , ROY K . Magnetic tunnel junction based long-term short-term stochastic synapse for a spiking neural network with on-chip STDP learning [J ] . Scientific Reports , 2016 , 6 : 29545 .

SENGUPTA A , PANDA P , WIJESINGHE P , et al . Magnetic tunnel junction mimics stochastic cortical spiking neurons [J ] . Scientific Reports , 2016 , 6 : 30039 .

KANEKO Y , NISHITANI Y , UEDA M . Ferroelectric artificial synapses for recognition of a multishaded image [J ] . IEEE Transactions on Electron Devices , 2014 , 61 ( 8 ): 2827 - 2833 .

KIM M K , LEE J S . Ferroelectric analog synaptic transistors [J ] . Nano Letters , 2019 , 19 ( 3 ): 2044 - 2050 .

JANG S , JANG S , LEE E H , et al . Ultrathin conformable organic artificial synapse for wearable intelligent device applications [J ] . ACS Applied Materials & Interfaces , 2019 , 11 ( 1 ): 1071 - 1080 .

MIAN M R , CHEN H Y , CAO R , et al . Insights into catalytic hydrolysis of organophosphonates at M-OH sites of azolate-based metal organic frameworks [J ] . Journal of the American Chemical Society , 2021 , 143 ( 26 ): 9893 - 9900 .

CAO R R , ZHANG X M , LIU S , et al . Compact artificial neuron based on anti-ferroelectric transistor [J ] . Nature Communications , 2022 , 13 : 7018 .

KIM K , CHEN C L , TRUONG Q , et al . A carbon nanotube synapse with dynamic logic and learning [J ] . Advanced Materials , 2013 , 25 ( 12 ): 1693 - 1698 .

ZHU L Q , WAN C J , GUO L Q , et al . Artificial synapse network on inorganic proton conductor for neuromorphic systems [J ] . Nature Communications , 2014 , 5 : 3158 .

YANG Y , WEN J , GUO L Q , et al . Long-term synaptic plasticity emulated in modified graphene oxide electrolyte gated IZO-based thin-film transistors [J ] . ACS Applied Materials & Interfaces , 2016 , 8 ( 44 ): 30281 - 30286 .

JIANG J E , GUO J J , WAN X A , et al . 2D MoS2 neuromorphic devices for brain-like computational systems [J ] . Small , 2017 , 13 ( 29 ): 1700933 .

FENG P , XU W W , YANG Y , et al . Printed neuromorphic devices based on printed carbon nanotube thin-film transistors [J ] . Advanced Functional Materials , 2017 , 27 ( 5 ): 1604447 .

JOHN R A , TIWARI N , CHEN Y Y , et al . Ultralow power dual-gated subthreshold oxide neuristors: An enabler for higher order neuronal temporal correlations [J ] . ACS Nano , 2018 , 12 ( 11 ): 11263 - 11273 .

HE Y L , NIE S , LIU R , et al . Spatiotemporal information processing emulated by multiterminal neuro-transistor networks [J ] . Advanced Materials , 2019 , 31 ( 21 ): e1900903 .

董哲康 . 基于忆阻器的电路分析及其在神经形态系统中的应用 [D ] . 杭州 : 浙江大学 , 2019 .

DONG Z K . Memristor based Circuit Analysis and its Applications in Neuromorphic Systems [D ] . Hangzhou : Zhejiang University , 2019 . (in Chinese)

刘益春 , 林亚 , 王中强 , 等 . 氧化物基忆阻型神经突触器件 [J ] . 物理学报 , 2019 , 68 ( 16 ): 119 - 137 .

LIU Y C , LIN Y , WANG Z Q , et al . Oxide-based memristive neuromorphic synaptic devices [J ] . Acta Physica Sinica , 2019 , 68 ( 16 ): 119 - 137 . (in Chinese)

YANG J J , PICKETT M D , LI X M , et al . Memristive switching mechanism for metal/oxide/metal nanodevices [J ] . Nature Nanotechnology , 2008 , 3 ( 7 ): 429 - 433 .

顾野 . 忆阻器件的特性与电路应用研究 [D ] . 成都 : 电子科技大学 , 2015 .

GU Y . Research on Memristor Device Characteristics and Circuit Applications [D ] . Chengdu : University of Electronic Science and Technology of China , 2015 . (in Chinese)

DONG Z K , LI C Y , QI D L , et al . Multiple memristor circuit parametric fault diagnosis using feedback-control doublet generator [J ] . IEEE Access , 2016 , 4 : 2604 - 2614 .

VOURKAS I , SIRAKOULIS G C . A novel design and modeling paradigm for memristor-based crossbar circuits [J ] . IEEE Transactions on Nanotechnology , 2012 , 11 ( 6 ): 1151 - 1159 .

KVATINSKY S , RAMADAN M , FRIEDMAN E G , et al . VTEAM: A general model for voltage-controlled memristors [J ] . IEEE Transactions on Circuits and Systems II: Express Briefs , 2015 , 62 ( 8 ): 786 - 790 .

KVATINSKY S , FRIEDMAN E G , KOLODNY A , et al . TEAM: ThrEshold adaptive memristor model [J ] . IEEE Transactions on Circuits and Systems I: Regular Papers , 2013 , 60 ( 1 ): 211 - 221 .

WANG X B , CHEN Y R , XI H W , et al . Spintronic memristor through spin-torque-induced magnetization motion [J ] . IEEE Electron Device Letters , 2009 , 30 ( 3 ): 294 - 297 .

HU M , LI H , CHEN Y R , et al . Geometry variations analysis of TiO2 thin-film and spintronic memristors [C ] // 16th Asia and South Pacific Design Automation Conference . Piscataway : IEEE , 2011 : 25 - 30 .

CHANG T , JO S H , KIM K H , et al . Synaptic behaviors and modeling of a metal oxide memristive device [J ] . Applied Physics A , 2011 , 102 ( 4 ): 857 - 863 .

YAN X B , PEI Y F , CHEN H W , et al . Self-assembled networked PbS distribution quantum dots for resistive switching and artificial synapse performance boost of memristors [J ] . Advanced Materials , 2019 , 31 ( 7 ): 1805284 .

SLAUGHTER J M . Materials for magnetoresistive random access memory [J ] . Annual Review of Materials Research , 2009 , 39 : 277 - 296 .

WANG Z R , JOSHI S , SAVEL'EV S , et al . Fully memristive neural networks for pattern classification with unsupervised learning [J ] . Nature Electronics , 2018 , 1 ( 2 ): 137 - 145 .

CAI F X , CORRELL J M , LEE S H , et al . A fully integrated reprogrammable memristor-CMOS system for efficient multiply-accumulate operations [J ] . Nature Electronics , 2019 , 2 ( 7 ): 290 - 299 .

IELMINI D , AMBROGIO S , MILO V , et al . Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning [C ] // 2016 IEEE International Symposium on Circuits and Systems (ISCAS) . Piscataway : IEEE , 2016 : 1386 - 1389 .

CHEN P Y , YU S M . Partition SRAM and RRAM based synaptic arrays for neuro-inspired computing [C ] // 2016 IEEE International Symposium on Circuits and Systems (ISCAS) . Piscataway : IEEE , 2016 : 2310 - 2313 .

KIM S G , HAN J S , KIM H , et al . Recent advances in memristive materials for artificial synapses [J ] . Advanced Materials Technologies , 2018 , 3 ( 12 ): 1800457 .

TSAI H , AMBROGIO S , NARAYANAN P , et al . Recent progress in analog memory-based accelerators for deep learning [J ] . Journal of Physics D: Applied Physics , 2018 , 51 ( 28 ): 283001 .

SUNG C , HWANG H , YOO I K . Perspective: A review on memristive hardware for neuromorphic computation [J ] . Journal of Applied Physics , 2018 , 124 ( 15 ): 151903 .

CRISTIANO G , GIORDANO M , AMBROGIO S , et al . Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance [J ] . Journal of Applied Physics , 2018 , 124 ( 15 ): 151901 .

LI C , BELKIN D , LI Y N , et al . Efficient and self-adaptive in situ learning in multilayer memristor neural networks [J ] . Nature Communications , 2018 , 9 : 2385 .

YAO P , WU H Q , GAO B , et al . Face classification using electronic synapses [J ] . Nature Communications , 2017 , 8 : 15199 .

BAYAT F M , PREZIOSO M , CHAKRABARTI B , et al . Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits [J ] . Nature Communications , 2018 , 9 : 2331 .

KWAK M , PARK J , WOO J , et al . Implementation of convolutional kernel function using 3-D TiOx resistive switching devices for image processing [J ] . IEEE Transactions on Electron Devices , 2018 , 65 ( 10 ): 4716 - 4718 .

YAKOPCIC C , ALOM M Z , TAHA T M . Memristor crossbar deep network implementation based on a Convolutional neural network [C ] // 2016 International Joint Conference on Neural Networks (IJCNN) . Piscataway : IEEE , 2016 : 963 - 970 .

GARBIN D , VIANELLO E , BICHLER O , et al . HfO2-based OxRAM devices as synapses for convolutional neural networks [J ] . IEEE Transactions on Electron Devices , 2015 , 62 ( 8 ): 2494 - 2501 .

GOKMEN T , ONEN M , HAENSCH W . Training deep convolutional neural networks with resistive cross-point devices [J ] . Frontiers in Neuroscience , 2017 , 11 : 538 .

LI C , WANG Z R , RAO M Y , et al . Long short-term memory networks in memristor crossbar arrays [J ] . Nature Machine Intelligence , 2019 , 1 ( 1 ): 49 - 57 .

TSAI H , AMBROGIO S , MACKIN C , et al . Inference of Long-Short Term Memory networks at software-equivalent accuracy using 2.5M analog Phase Change Memory devices [C ] // 2019 Symposium on VLSI Technology . Piscataway : IEEE , 2019 : T82 - T83 .

蔡佳林 . 基于自旋电子器件的神经形态器件研究 [D ] . 合肥 : 中国科学技术大学 , 2019 .

CAI J L . Research on Neuromorphic Devices Based on Spintronics [D ] . Hefei : University of Science and Technology of China , 2019 . (in Chinese)

YUASA S , FUKUSHIMA A , YAKUSHIJI K , et al . Future prospects of MRAM technologies [C ] // 2013 IEEE International Electron Devices Meeting . Piscataway : IEEE , 2014 : 3.1.1- 3 . 1 . 4 .

FUKUSHIMA A , SEKI T , YAKUSHIJI K , et al . Spin dice: A scalable truly random number generator based on spintronics [J ] . Applied Physics Express , 2014 , 7 ( 8 ): 083001 .

VINCENT A F , LARROQUE J , LOCATELLI N , et al . Stochastic memristive synapses from spin-transfer torque magnetic tunnel junctions [C ] // 2015 IEEE International Magnetics Conference (INTERMAG) . Piscataway : IEEE , 2015 : 1 - 1 .

SENGUPTA A , PARSA M , HAN B , et al . Probabilistic deep spiking neural systems enabled by magnetic tunnel junction [J ] . IEEE Transactions on Electron Devices , 2016 , 63 ( 7 ): 2963 - 2970 .

LUO Z , WANG Z J , GUAN Z Y , et al . High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing [J ] . Nature Communications , 2022 , 13 : 699 .

PAINKRAS E , PLANA L A , GARSIDE J , et al . SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation [J ] . IEEE Journal of Solid-State Circuits , 2013 , 48 ( 8 ): 1943 - 1953 .

AKOPYAN F , SAWADA J , CASSIDY A , et al . TrueNorth: Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip [J ] . IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2015 , 34 ( 10 ): 1537 - 1557 .

SHIBATA T , OHMI T . An intelligent MOS transistor featuring gate-level weighted sum and threshold operations [C ] // International Electron Devices Meeting 1991 [Technical Digest] . Piscataway : IEEE , 2002 : 919 - 922 .

GUO X , BAYAT F M , PREZIOSO M , et al . Temperature-insensitive analog vector-by-matrix multiplier based on 55 nm NOR flash memory cells [C ] // 2017 IEEE Custom Integrated Circuits Conference (CICC) . Piscataway : IEEE , 2017 : 1 - 4 .

JACKSON B L , RAJENDRAN B , CORRADO G S , et al . Nanoscale electronic synapses using phase change devices [J ] . ACM Journal on Emerging Technologies in Computing Systems , 2013 , 9 ( 2 ): 12 .

MO F , TAGAWA Y , JIN C J , et al . Low-voltage operating ferroelectric FET with ultrathin IGZO channel for high-density memory application [J ] . IEEE Journal of the Electron Devices Society , 2020 , 8 : 717 - 723 .

徐丽莹 , 杨玉超 , 黄如 . 基于忆阻器的非易失逻辑研究前沿 [J ] . 中国基础科学 , 2019 , 21 ( 2 ): 1 - 11, 27, 63 .

XU L Y , YANG Y C , HUANG R . Research frontier at memristor-based nonvolatile logic [J ] . China Basic Science , 2019 , 21 ( 2 ): 1 - 11, 27, 63 . (in Chinese)

GE R J , WU X H , KIM M , et al . Atomristor: Nonvolatile resistance switching in atomic sheets of transition metal dichalcogenides [J ] . Nano Letters , 2018 , 18 ( 1 ): 434 - 441 .

李锟 , 曹荣荣 , 孙毅 , 等 . 基于忆阻器的感存算一体技术研究进展 [J ] . 微纳电子与智能制造 , 2019 , 1 ( 4 ): 87 - 102 .

LI K , CAO R R , SUN Y , et al . Research progress on the fused technology of sensing, storage and computing based on memristor [J ] . Micro/Nano Electronics and Intelligent Manufacturing , 2019 , 1 ( 4 ): 87 - 102 . (in Chinese)

张晨曦 , 陈艳 , 仪明东 , 等 . 基于忆阻器模拟的突触可塑性的研究进展 [J ] . 中国科学: 信息科学 , 2018 , 48 ( 2 ): 115 - 142 .

ZHANG C X , CHEN Y , YI M D , et al . Recent progress in memristors for stimulating synaptic plasticity [J ] . Scientia Sinica (Informationis) , 2018 , 48 ( 2 ): 115 - 142 . (in Chinese)

张鑫 , 夏天 , 叶葱 , 等 . 基于忆阻器实现多样化STDP学习规则的突触电路设计 [J ] . 中国科学: 技术科学 , 2021 , 51 ( 1 ): 89 - 98 .

ZHANG X , XIA T , YE C , et al . Realizing diverse STDP learning rules in synaptic circuit based on memristor [J ] . Scientia Sinica (Technologica) , 2021 , 51 ( 1 ): 89 - 98 . (in Chinese)

陆骐峰 , 孙富钦 , 王子豪 , 等 . 柔性人工突触: 面向智能人机交互界面和高效率神经网络计算的基础器件 [J ] . 材料导报 , 2020 , 34 ( 1 ): 1022 - 1049 .

LU Q F , SUN F Q , WANG Z H , et al . Recent advances in flexible artificial synapses towards intelligent human-machine interface and neuromorphic computation systems [J ] . Materials Reports , 2020 , 34 ( 1 ): 1022 - 1049 . (in Chinese)

王洋昊 , 刘昌 , 黄如 , 等 . 神经形态器件研究进展与未来趋势 [J ] . 科学通报 , 2020 , 65 ( 10 ): 904 - 915 .

WANG Y H , LIU C , HUANG R , et al . Progresses and outlook in neuromorphic devices [J ] . Chinese Science Bulletin , 2020 , 65 ( 10 ): 904 - 915 . (in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multistability Analysis of a Dual-Memristor Circuit Based on Hyperbolic Function

Related Author

MIN Fu-hong

WANG Zhu-lin

CAO Yi

WANG En-rong

Related Institution

School of Electrical and Automation Engineering, Nanjing Normal University

China Nuclear Power Operation Technology Corporation, LTD.

School of Electrical and Automation Engineering Nanjing Normal University Nanjing Jiangsu China

China Nuclear Power Operation Technology Corporation LTD. Wuhan Hubei China

⁰