Photothermally Activated Artificial Neuromorphic Synapses

被引:8
|
作者
Blankenship, Brian W. [1 ]
Li, Runxuan [1 ]
Guo, Ruihan [2 ]
Zhao, Naichen [1 ]
Shin, Jaeho [3 ]
Yang, Rundi [1 ]
Ko, Seung Hwan [3 ]
Wu, Junqiao [2 ]
Rho, Yoonsoo [1 ,4 ,5 ]
Grigoropoulos, Costas [1 ]
机构
[1] Univ Calif Berkeley, Dept Mech Engn, Laser Thermal Lab, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Dept Mat Sci & Engn, Berkeley, CA 94720 USA
[3] Seoul Natl Univ, Dept Mech Engn, Appl Nano & Thermal Sci Lab, Seoul 08826, South Korea
[4] Lawrence Livermore Natl Lab, Phys & Life Sci, Livermore, CA 94550 USA
[5] Lawrence Livermore Natl Lab, NIF & Photon Sci, Livermore, CA 94550 USA
关键词
neuromorphic devices; vanadium dioxide; artificialneural networks; photothermal electronics; PATTERN-RECOGNITION; ORGANIZATION; TRANSITION;
D O I
10.1021/acs.nanolett.3c02681
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. To incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to "activate" these neurons and store "memory" by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.
引用
收藏
页码:9020 / 9025
页数:6
相关论文
共 50 条
  • [1] Neuromorphic synapses for artificial dendrites
    Westerman, C
    Northmore, DPM
    Elias, JG
    ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING, 1997, 13 (1-2) : 167 - 184
  • [2] Neuromorphic Synapses for Artificial Dendrites
    Wayne C. Westerman
    David P. M. Northmore
    John. G. Elias
    Analog Integrated Circuits and Signal Processing, 1997, 13 : 167 - 184
  • [3] Multimodal Artificial Synapses for Neuromorphic Application
    Li, Runze
    Yue, Zengji
    Luan, Haitao
    Dong, Yibo
    Chen, Xi
    Gu, Min
    RESEARCH, 2024, 7
  • [4] Memristive Artificial Synapses for Neuromorphic Computing
    Huang, Wen
    Xia, Xuwen
    Zhu, Chen
    Steichen, Parker
    Quan, Weidong
    Mao, Weiwei
    Yang, Jianping
    Chu, Liang
    Li, Xing'ao
    NANO-MICRO LETTERS, 2021, 13 (01)
  • [5] Memristive Artificial Synapses for Neuromorphic Computing
    Wen Huang
    Xuwen Xia
    Chen Zhu
    Parker Steichen
    Weidong Quan
    Weiwei Mao
    Jianping Yang
    Liang Chu
    Xing'ao Li
    Nano-Micro Letters, 2021, 13 (05) : 224 - 251
  • [6] Memristive Artificial Synapses for Neuromorphic Computing
    Wen Huang
    Xuwen Xia
    Chen Zhu
    Parker Steichen
    Weidong Quan
    Weiwei Mao
    Jianping Yang
    Liang Chu
    Xing’ao Li
    Nano-Micro Letters, 2021, 13
  • [7] Neuromorphic Skin Based on Emerging Artificial Synapses
    Lee, Yeongjun
    Oh, Jin Young
    Lee, Tae-Woo
    ADVANCED MATERIALS TECHNOLOGIES, 2022, 7 (12):
  • [8] Light-Emitting Artificial Synapses for Neuromorphic Computing
    Zhu, Chen
    Huang, Wen
    Li, Wei
    Yu, Xuegong
    Li, Xing'ao
    RESEARCH, 2022, 2022
  • [9] Skyrmion-based artificial synapses for neuromorphic computing
    Song, Kyung Mee
    Jeong, Jae-Seung
    Pan, Biao
    Zhang, Xichao
    Xia, Jing
    Cha, Sunkyung
    Park, Tae-Eon
    Kim, Kwangsu
    Finizio, Simone
    Raabe, Joerg
    Chang, Joonyeon
    Zhou, Yan
    Zhao, Weisheng
    Kang, Wang
    Ju, Hyunsu
    Woo, Seonghoon
    NATURE ELECTRONICS, 2020, 3 (03) : 148 - 155
  • [10] Skyrmion-based artificial synapses for neuromorphic computing
    Kyung Mee Song
    Jae-Seung Jeong
    Biao Pan
    Xichao Zhang
    Jing Xia
    Sunkyung Cha
    Tae-Eon Park
    Kwangsu Kim
    Simone Finizio
    Jörg Raabe
    Joonyeon Chang
    Yan Zhou
    Weisheng Zhao
    Wang Kang
    Hyunsu Ju
    Seonghoon Woo
    Nature Electronics, 2020, 3 : 148 - 155