Memristive Artificial Synapses for Neuromorphic Computing

被引:161
|
作者
Huang, Wen [1 ,2 ]
Xia, Xuwen [1 ,2 ]
Zhu, Chen [5 ,6 ]
Steichen, Parker [7 ]
Quan, Weidong [1 ,2 ]
Mao, Weiwei [1 ,2 ]
Yang, Jianping [1 ,2 ]
Chu, Liang [1 ,2 ]
Li, Xing'ao [1 ,2 ,3 ,4 ]
机构
[1] Nanjing Univ Posts & Telecommun NJUPT, New Energy Technol Engn Lab Jiangsu Prov, Nanjing 210023, Peoples R China
[2] Nanjing Univ Posts & Telecommun NJUPT, Sch Sci, Nanjing 210023, Peoples R China
[3] Nanjing Univ Posts & Telecommun NUPT, Key Lab Organ Elect & Informat Displays, Sch Mat Sci & Engn, Jiangsu Natl Synergist Innovat Ctr Adv Mat, 9 Wenyuan Rd, Nanjing 210023, Peoples R China
[4] Nanjing Univ Posts & Telecommun NUPT, Inst Adv Mat, Sch Mat Sci & Engn, Jiangsu Natl Synergist Innovat Ctr Adv Mat, 9 Wenyuan Rd, Nanjing 210023, Peoples R China
[5] Nanjing Univ Posts & Telecommun NJUPT, Coll Elect & Opt Engn, Nanjing 210023, Peoples R China
[6] Nanjing Univ Posts & Telecommun NJUPT, Coll Microelect, Nanjing 210023, Peoples R China
[7] Univ Washington, Dept Mat Sci & Engn, Seattle, WA 98195 USA
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Synaptic devices; Neuromorphic computing; Electrical pulses; Optical pulses; Photoelectric synergetic effects; PHASE-CHANGE MEMORY; 2-DIMENSIONAL MATERIALS; SYNAPTIC DEVICE; SILICON NANOCRYSTALS; PEROVSKITE MATERIALS; RECENT PROGRESS; DOMAIN-WALLS; SOLAR-CELLS; PLASTICITY; ELECTRONICS;
D O I
10.1007/s40820-021-00618-2
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
HighlightsSynaptic devices that mimic synaptic functions are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.The working mechanisms, progress, and application scenarios of synaptic devices based on electrical and optical signals are compared and analyzed.The performances and future development of various synaptic devices that could be significant for building efficient neuromorphic systems are prospected. AbstractNeuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
引用
收藏
页数:28
相关论文
共 50 条
  • [21] A memristive diode for neuromorphic computing
    Wang, Xiaolei
    Shao, Qi
    Ku, Pui Sze
    Ruotolo, Antonio
    MICROELECTRONIC ENGINEERING, 2015, 138 : 7 - 11
  • [22] Neuromorphic computing with memristive devices
    Ma, Wen
    Zidan, Mohammed A.
    Lu, Wei D.
    SCIENCE CHINA-INFORMATION SCIENCES, 2018, 61 (06)
  • [23] An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing
    Kunwar, Sundar
    Jernigan, Zachary
    Hughes, Zach
    Somodi, Chase
    Saccone, Michael D. D.
    Caravelli, Francesco
    Roy, Pinku
    Zhang, Di
    Wang, Haiyan
    Jia, Quanxi
    MacManus-Driscoll, Judith L. L.
    Kenyon, Garrett
    Sornborger, Andrew
    Nie, Wanyi
    Chen, Aiping
    ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (08)
  • [24] Bioartificial Synapses for Neuromorphic Computing
    Wang, Lu
    Wei, Shutao
    Xie, Jiachu
    Wen, Dianzhong
    ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 2023, 11 (06) : 2229 - 2237
  • [25] Enhancing plasticity in optoelectronic artificial synapses: A pathway to efficient neuromorphic computing
    Yuan, Jiahao
    Wu, Chao
    Wang, Shunli
    Wu, Fengmin
    Tan, Chee Keong
    Guo, Daoyou
    APPLIED PHYSICS LETTERS, 2024, 124 (02)
  • [26] Dimensionality Dependent Plasticity in Halide Perovskite Artificial Synapses for Neuromorphic Computing
    Kim, Sung-Il
    Lee, Yeongjun
    Park, Min-Ho
    Go, Gyeong-Tak
    Kim, Young-Noon
    Xu, Wentao
    Lee, Hyeon-Dong
    Kim, Hobeom
    Seo, Dae-Gyo
    Lee, Wanhee
    Lee, Tae-Woo
    ADVANCED ELECTRONIC MATERIALS, 2019, 5 (09)
  • [27] Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses
    Fan, Zhao-Yuan
    Tang, Zhenhua
    Fang, Jun-Lin
    Jiang, Yan-Ping
    Liu, Qiu-Xiang
    Tang, Xin-Gui
    Zhou, Yi-Chun
    Gao, Ju
    NANOMATERIALS, 2024, 14 (07)
  • [28] MoSr-based quantum dot artificial synapses for neuromorphic computing
    Liu, Gongjie
    Liu, Haoqi
    Fan, Feifan
    Gu, Yuefeng
    Wei, Lisi
    Xiang, Xiaolin
    Wang, Yuhao
    Li, Qiuhong
    MATERIALS TODAY PHYSICS, 2025, 53
  • [29] Integrated neuromorphic computing networks by artificial spin synapses and spin neurons
    Yang, Seungmo
    Shin, Jeonghun
    Kim, Taeyoon
    Moon, Kyoung-Woong
    Kim, Jaewook
    Jang, Gabriel
    Hyeon, Da Seul
    Yang, Jungyup
    Hwang, Chanyong
    Jeong, YeonJoo
    Hong, Jin Pyo
    NPG ASIA MATERIALS, 2021, 13 (01)
  • [30] Integrated neuromorphic computing networks by artificial spin synapses and spin neurons
    Seungmo Yang
    Jeonghun Shin
    Taeyoon Kim
    Kyoung-Woong Moon
    Jaewook Kim
    Gabriel Jang
    Da Seul Hyeon
    Jungyup Yang
    Chanyong Hwang
    YeonJoo Jeong
    Jin Pyo Hong
    NPG Asia Materials, 2021, 13