Photo-induced non-volatile VO2 phase transition for neuromorphic ultraviolet sensors

Ge Li, Donggang Xie, Hai Zhong, Ziye Zhang, Xingke Fu, Qingli Zhou, Qiang Li, Hao Ni, Jiaou Wang, Er-jia Guo, Meng He, Can Wang, Guozhen Yang, Kuijuan Jin () and Chen Ge ()
Additional contact information
Ge Li: Institute of Physics, Chinese Academy of Sciences
Donggang Xie: Institute of Physics, Chinese Academy of Sciences
Hai Zhong: Institute of Physics, Chinese Academy of Sciences
Ziye Zhang: Institute of Physics, Chinese Academy of Sciences
Xingke Fu: Institute of Physics, Chinese Academy of Sciences
Qingli Zhou: Capital Normal University
Qiang Li: University-Industry Joint Center for Ocean Observation and Broadband Communication, State Key Laboratory of Bio-Fibers and Eco-Textiles Qingdao University
Hao Ni: China University of Petroleum (East China)
Jiaou Wang: Institute of High Energy Physics, Chinese Academy of Sciences
Er-jia Guo: Institute of Physics, Chinese Academy of Sciences
Meng He: Institute of Physics, Chinese Academy of Sciences
Can Wang: Institute of Physics, Chinese Academy of Sciences
Guozhen Yang: Institute of Physics, Chinese Academy of Sciences
Kuijuan Jin: Institute of Physics, Chinese Academy of Sciences
Chen Ge: Institute of Physics, Chinese Academy of Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO2 films by oxygen stoichiometry engineering under ultraviolet irradiation. Based on the reversible regulation of VO2 films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.

Date: 2022
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DOI: 10.1038/s41467-022-29456-5

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