Nanoscale Memristor Device as Synapse in Neuromorphic Systems

被引:3345
作者
Jo, Sung Hyun [1 ]
Chang, Ting [1 ]
Ebong, Idongesit [1 ]
Bhadviya, Bhavitavya B. [1 ]
Mazumder, Pinaki [1 ]
Lu, Wei [1 ]
机构
[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
Nanoelectronics; neuromorphic system; memristor; synaptic adaptation; spike-timing dependent plasticity; RESISTANCE; MODEL;
D O I
10.1021/nl904092h
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
引用
收藏
页码:1297 / 1301
页数:5
相关论文
共 50 条
  • [31] Physically Transient Memristor Synapse Based on Embedding Magnesium Nanolayer in Oxide for Security Neuromorphic Electronics
    Dang, Bingjie
    Wu, Quantan
    Sun, Jing
    Zhao, Momo
    Wang, Saisai
    Song, Fang
    Yang, Mei
    Ma, Xiaohua
    Wang, Hong
    Hao, Yue
    IEEE ELECTRON DEVICE LETTERS, 2019, 40 (08) : 1265 - 1268
  • [32] Memristor Neural Network Training with Clock Synchronous Neuromorphic System
    Jo, Sumin
    Sun, Wookyung
    Kim, Bokyung
    Kim, Sunhee
    Park, Junhee
    Shin, Hyungsoon
    MICROMACHINES, 2019, 10 (06)
  • [33] SPICE Study of STDP Characteristics in a Drift and Diffusive Memristor-Based Synapse for Neuromorphic Computing
    Hu, Suman
    Kang, Jaehyun
    Kim, Taeyoon
    Lee, Suyoun
    Park, Jong Keuk
    Kim, Inho
    Kim, Jaewook
    Kwak, Joon Young
    Park, Jongkil
    Kim, Gyu-Tae
    Choi, Shinhyun
    Jeong, Yeonjoo
    IEEE ACCESS, 2022, 10 : 6381 - 6392
  • [34] A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
    Nair, Manu, V
    Muller, Lorenz K.
    Indiveri, Giacomo
    NANO FUTURES, 2017, 1 (03)
  • [35] Advent of Memristor based synapses on Neuromorphic Engineering
    Vidya, S.
    Ahmed, Mohammed Riyaz
    2017 INTERNATIONAL CONFERENCE ON MICROELECTRONIC DEVICES, CIRCUITS AND SYSTEMS (ICMDCS), 2017,
  • [36] Modeling triplet spike timing dependent plasticity using a hybrid TFT-memristor neuromorphic synapse
    Aghnout, Soraya
    Karimi, Gholamreza
    INTEGRATION-THE VLSI JOURNAL, 2019, 64 : 184 - 191
  • [37] Artificial synapse based on a tri-layer AlN/AlScN/AlN stacked memristor for neuromorphic computing
    Dai, Xinhuan
    Hua, Qilin
    Jiang, Chunsheng
    Long, Yong
    Dong, Zilong
    Shi, Yuanhong
    Huang, Tianci
    Li, Haotian
    Meng, Haixing
    Yang, Yang
    Wei, Ruilai
    Shen, Guozhen
    Hu, Weiguo
    NANO ENERGY, 2024, 124
  • [38] Artificial Synapse Consisted of TiSbTe/SiCx:H Memristor with Ultra-high Uniformity for Neuromorphic Computing
    Chen, Liangliang
    Ma, Zhongyuan
    Leng, Kangmin
    Chen, Tong
    Hu, Hongsheng
    Yang, Yang
    Li, Wei
    Xu, Jun
    Xu, Ling
    Chen, Kunji
    NANOMATERIALS, 2022, 12 (12)
  • [39] Optoelectronic memristor for neuromorphic computing
    Xue, Wuhong
    Ci, Wenjuan
    Xu, Xiao-Hong
    Liu, Gang
    CHINESE PHYSICS B, 2020, 29 (04)
  • [40] Artificial synapse network on inorganic proton conductor for neuromorphic systems
    Zhu, Li Qiang
    Wan, Chang Jin
    Guo, Li Qiang
    Shi, Yi
    Wan, Qing
    NATURE COMMUNICATIONS, 2014, 5