Journal of Inorganic Materials ›› 2023, Vol. 38 ›› Issue (10): 1149-1162.DOI: 10.15541/jim20230066

Special Issue: 【信息功能】神经形态材料与器件(202310)

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Oxide Memristors for Brain-inspired Computing

ZHUGE Xia1(), ZHU Renxiang1, WANG Jianmin1, WANG Jingrui1, ZHUGE Fei2,3,4,5()   

  1. 1. School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
    2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
    3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
    4. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China
    5. Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
  • Received:2023-02-09 Revised:2023-03-01 Published:2023-10-20 Online:2023-03-24
  • Contact: ZHUGE Fei, professor. E-mail:
  • About author:ZHUGE Xia (1979-), female, PhD, lecturer. E-mail:
  • Supported by:
    National Natural Science Foundation of China(U20A20209);National Natural Science Foundation of China(61874125);Strategic Priority Research Program of Chinese Academy of Sciences(XDB32050204);State Key Laboratory for Environment-Friendly Energy Materials(20kfhg09);Ningbo Natural Science Foundation of China(2021J139);State Key Laboratory of Silicon Materials(SKL2021-03)


Brain-inspired neuromorphic computing refers to simulation of the structure and functionality of the human brain via the integration of electronic or photonic devices. Artificial synapses are the most abundant computation element in the brain-inspired system. Memristors are considered to be ideal devices for artificial synapse applications because of their high scalability and low power consumption. Based on Ohm’s law and Kirchhoff’s law, memristor crossbar arrays can perform parallel multiply-accumulate operations in situ, leading to analogue computing with greatly improved speed and energy efficiency. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with CMOS processes. This work reviews the research progress of oxide memristors for brain-inspired computing, mainly focusing on their resistance switching mechanisms, device structures and performances. These devices fall into three categories: electrical memristors, memristors controlled via both electrical and optical stimuli, and all-optically controlled memristors. The working mechanisms of electrical memristors are commonly related to microstructure change and Joule heat that are detrimental to device stability. The device performance can be improved by optimizing device structure and material composition. Tuning the device conductance with optical signals can avoid microstructure change and Joule heat as well as reducing energy consumption, thus making it possible to address the stability problem. In addition, optically controlled memristors can directly response to external light stimulus enabling integrated sensing-computing-memoring within single devices, which are expected to be used for developing next-generation vision sensors. Hence, the realization of all-optically controlled memristors opens a new window for research and applications of memristors.

Key words: oxide memristor, optoelectronic device, artificial synapse, brain-inspired neuromorphic computing, review

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