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Optoelectronic Perovskite Synapses for Neuromorphic Computing
Author(s) -
Ma Fumin,
Zhu Yangbin,
Xu Zhongwei,
Liu Yang,
Zheng Xiaojing,
Ju Songman,
Li Qianqian,
Ni Ziquan,
Hu Hailong,
Chai Yang,
Wu Chaoxing,
Kim Tae Whan,
Li Fushan
Publication year - 2020
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201908901
Subject(s) - neuromorphic engineering , materials science , perovskite (structure) , synaptic plasticity , photonics , memristor , neural facilitation , computer science , optoelectronics , neuroscience , nanotechnology , artificial intelligence , artificial neural network , electronic engineering , engineering , biology , biochemistry , receptor , chemical engineering
Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.