中国物理B ›› 2023, Vol. 32 ›› Issue (12): 128401-128401.doi: 10.1088/1674-1056/ad02e8

所属专题： SPECIAL TOPIC—Post-Moore era: Materials and device physics

• • 上一篇    下一篇

Reconfigurable Mott electronics for homogeneous neuromorphic platform

Zhen Yang(杨振)1, Ying-Ming Lu(路英明)1, and Yu-Chao Yang(杨玉超)1,2,3,4,†   

  1. 1 Beijing Advanced Innovation Center for Integrated Circuit, School of Integrated Circuits, Peking University, Beijing 100871, China;
    2 School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China;
    3 Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing 100871, China;
    4 Center for Brain Inspired Intelligence, Chinese Institute for Brain Research(CIBR), Beijing 102206, China
  • 收稿日期:2023-08-05 修回日期:2023-09-28 接受日期:2023-10-13 出版日期:2023-11-14 发布日期:2023-11-30
  • 通讯作者: Yu-Chao Yang E-mail:yuchaoyang@pku.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China(Grant Nos.61925401, 92064004, 61927901, and 92164302) and the 111 Project (Grant No.B18001). Y. Y. acknowledges support from the Fok Ying-Tong Education Foundation and the Tencent Foundation through the XPLORER PRIZE. The authors acknowledge the support of TOF-SIMS characterization by Dr. Tinglu Song and the first-principal computation by Dr. Bing Zheng from Beijing Institute of Technology.

Reconfigurable Mott electronics for homogeneous neuromorphic platform

Zhen Yang(杨振)1, Ying-Ming Lu(路英明)1, and Yu-Chao Yang(杨玉超)1,2,3,4,†   

  1. 1 Beijing Advanced Innovation Center for Integrated Circuit, School of Integrated Circuits, Peking University, Beijing 100871, China;
    2 School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China;
    3 Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing 100871, China;
    4 Center for Brain Inspired Intelligence, Chinese Institute for Brain Research(CIBR), Beijing 102206, China
  • Received:2023-08-05 Revised:2023-09-28 Accepted:2023-10-13 Online:2023-11-14 Published:2023-11-30
  • Contact: Yu-Chao Yang E-mail:yuchaoyang@pku.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China(Grant Nos.61925401, 92064004, 61927901, and 92164302) and the 111 Project (Grant No.B18001). Y. Y. acknowledges support from the Fok Ying-Tong Education Foundation and the Tencent Foundation through the XPLORER PRIZE. The authors acknowledge the support of TOF-SIMS characterization by Dr. Tinglu Song and the first-principal computation by Dr. Bing Zheng from Beijing Institute of Technology.

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摘要： To simplify the fabrication process and increase the versatility of neuromorphic systems, the reconfiguration concept has attracted much attention. Here, we developed a novel electrochemical VO2 (EC-VO2) device, which can be reconfigured as synapses or LIF neurons. The ionic dynamic doping contributed to the resistance changes of VO2, which enables the reversible modulation of device states. The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration. Based on the reconfigurable EC-VO2, the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons, whose final accuracy reached 91.92%.

关键词: Mott electronics, reconfigurable, neuromorphic computing, VO2

Abstract: To simplify the fabrication process and increase the versatility of neuromorphic systems, the reconfiguration concept has attracted much attention. Here, we developed a novel electrochemical VO2 (EC-VO2) device, which can be reconfigured as synapses or LIF neurons. The ionic dynamic doping contributed to the resistance changes of VO2, which enables the reversible modulation of device states. The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration. Based on the reconfigurable EC-VO2, the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons, whose final accuracy reached 91.92%.

Key words: Mott electronics, reconfigurable, neuromorphic computing, VO2

中图分类号:  (Electronic circuits)

  • 84.30.-r
85.40.-e (Microelectronics: LSI, VLSI, ULSI; integrated circuit fabrication technology) 87.85.dq (Neural networks) 81.07.-b (Nanoscale materials and structures: fabrication and characterization)