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Chin. Phys. B, 2020, Vol. 29(4): 048401    DOI: 10.1088/1674-1056/ab75da
Special Issue: SPECIAL TOPIC — Physics in neuromorphic devices
TOPICAL REVIEW—Physics in neuromorphic devices Prev   Next  

Optoelectronic memristor for neuromorphic computing

Wuhong Xue(薛武红)1,2, Wenjuan Ci(次文娟)1, Xiao-Hong Xu(许小红)1, Gang Liu(刘钢)2,3
1 Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education, School of Chemistry and Materials Science, Shanxi Normal University, Linfen 041004, China;
2 School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
3 College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001, China
Abstract  With the need of the internet of things, big data, and artificial intelligence, creating new computing architecture is greatly desired for handling data-intensive tasks. Human brain can simultaneously process and store information, which would reduce the power consumption while improve the efficiency of computing. Therefore, the development of brain-like intelligent device and the construction of brain-like computation are important breakthroughs in the field of artificial intelligence. Memristor, as the fourth fundamental circuit element, is an ideal synaptic simulator due to its integration of storage and processing characteristics, and very similar activities and the working mechanism to synapses among neurons which are the most numerous components of the brains. In particular, memristive synaptic devices with optoelectronic responding capability have the benefits of storing and processing transmitted optical signals with wide bandwidth, ultrafast data operation speed, low power consumption, and low cross-talk, which is important for building efficient brain-like computing networks. Herein, we review recent progresses in optoelectronic memristor for neuromorphic computing, including the optoelectronic memristive materials, working principles, applications, as well as the current challenges and the future development of the optoelectronic memristor.
Keywords:  memristor      optoelectronic      neuromorphic computing  
Received:  08 January 2020      Revised:  05 February 2020      Accepted manuscript online: 
PACS:  84.37.+q (Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.))  
  87.19.lw (Plasticity)  
  87.19.lv (Learning and memory)  
  84.35.+i (Neural networks)  
Fund: Project supported by the National Key R&D Program of China (Grant No. 2017YFB0405600), the National Natural Science Foundation of China (Grant Nos. 61674153, 61722407, 61974090, and 61904099), and the Natural Science Foundation of Shanghai, China (Grant No. 19ZR1474500).
Corresponding Authors:  Xiao-Hong Xu, Gang Liu     E-mail:  xuxh@sxnu.edu.cn;gang.liu@sjtu.edu.cn

Cite this article: 

Wuhong Xue(薛武红), Wenjuan Ci(次文娟), Xiao-Hong Xu(许小红), Gang Liu(刘钢) Optoelectronic memristor for neuromorphic computing 2020 Chin. Phys. B 29 048401

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