Artificial neurosynaptic device based on amorphous oxides for artificial neural network constructing

被引:1
|
作者
Chen, Qiujiang [1 ]
Yang, Ruqi [1 ]
Hu, Dunan [1 ]
Ye, Zhizhen [1 ]
Lu, Jianguo [1 ]
机构
[1] Zhejiang Univ, Sch Mat Sci & Engn, State Key Lab Silicon & Adv Semicond Mat, Hangzhou 310027, Peoples R China
关键词
SYNAPTIC PLASTICITY; SYNAPSES;
D O I
10.1039/d4tc01244e
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The von Neumann architecture places restrictions on how much energy conventional computers can use for artificial intelligence training. We created a three-terminal artificial neural synapse that is electronically controlled using amorphous InAlZnO, which was inspired by biological synapses. With electrical pulses of -5 V and 5 V, respectively, the device can produce excitatory postsynaptic current (EPSC) and inhibitory postsynaptic current (IPSC), demonstrating good biological synaptic characteristics. A single pulse can use as little as 456.69 pJ of energy. Meanwhile, the synaptic device will transition from the short-term plasticity (STP)/short-term depression (STD) state to the long-term plasticity (LTP)/long-term depression (LTD) state as the number of pulses grows, more closely mimicking the features of brain learning and memory. Furthermore, conductance stability of the synaptic device is maintained after several LTP-LTD cycles, and we built a three-layer artificial neural network (ANN) on the basis of this attribute. Its handwritten digit recognition accuracy reaches 92.26% after training it with the MINST dataset. The amorphous oxide artificial neural synapse device developed in this work will be crucial in addressing the issue of artificial intelligence training's energy consumption as well as the development of artificial neural networks in the future. An artificial synaptic device based on amorphous oxides is created inspired by biological synapse, and a three-layer artificial neural network is constructed using the data of a LTP-LTD circle.
引用
收藏
页码:9165 / 9174
页数:10
相关论文
共 50 条
  • [31] ELECTROMECHANICAL CONTROL BASED ON ARTIFICIAL NEURAL NETWORK
    Yu, Ming-Chao
    Li, Gong-Fa
    Sun, Ying
    Tao, Bo
    Jiang, Guo-Zhang
    Xu, Shuang
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2, 2018, : 644 - 649
  • [32] Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model
    Lidija J. Stamenković
    Davor Z. Antanasijević
    Mirjana Đ. Ristić
    Aleksandra A. Perić-Grujić
    Viktor V. Pocajt
    Air Quality, Atmosphere & Health, 2017, 10 : 15 - 23
  • [33] Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model
    Stamenkovic, Lidija J.
    Antanasijevic, Davor Z.
    Ristic, Mirjana D.
    Peric-Grujic, Aleksandra A.
    Pocajt, Viktor V.
    AIR QUALITY ATMOSPHERE AND HEALTH, 2017, 10 (01): : 15 - 23
  • [34] Artificial neural network modelling of crystallization temperatures of the Ni-P based amorphous alloys
    Keong, KG
    Sha, W
    Malinov, S
    MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2004, 365 (1-2): : 212 - 218
  • [35] Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology
    Meshref, Hossam
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2013, 4 (12) : 30 - 35
  • [36] Artificial neural network for constructing type Ia supernovae spectrum evolution model
    Cheng, Qiao-Bin
    Feng, Chao-Jun
    Zhai, Xiang-Hua
    Li, Xin-Zhou
    PHYSICAL REVIEW D, 2018, 97 (12)
  • [37] Network Host Cardinality Estimation Based on Artificial Neural Network
    Jie, Xu
    Lan Haoliang
    Wei, Ding
    Ao, Ju
    SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
  • [38] Artificial Neural Network Based Online Network Strength Estimation
    Hernandez, Alvaro J. M.
    Abhay, Dixit
    Lottes, Juergen
    Steger, Manuel
    2018 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE EUROPE (ISGT-EUROPE), 2018,
  • [39] Control of Left Ventricular Assist Device using artificial neural network
    Kim, S
    Kim, H
    Ryu, J
    Chung, S
    PROCEEDINGS OF THE 19TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 19, PTS 1-6: MAGNIFICENT MILESTONES AND EMERGING OPPORTUNITIES IN MEDICAL ENGINEERING, 1997, 19 : 1363 - 1366
  • [40] Using Artificial Neural Network for Error Reduction in a Nondispersive Thermopile Device
    Pham, Son
    Dinh, Anh
    IEEE SENSORS JOURNAL, 2020, 20 (12) : 6277 - 6286