Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir

Jie Lao, Mengge Yan, Bobo Tian*, Chunli Jiang, Chunhua Luo, Zhuozhuang Xie, Qiuxiang Zhu, Zhiqiang Bao, Ni Zhong, Xiaodong Tang, Linfeng Sun, Guangjian Wu, Jianlu Wang, Hui Peng*, Junhao Chu, Chungang Duan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

99 Citations (Scopus)

Abstract

A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in-sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self-powered Au/P(VDF-TrFE)/Cs2AgBiBr6/ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in-sensor RC system takes advantage of near-zero energy consumption in the reservoir, resulting in decades-time lower training costs than a conventional neural network. This work paves the way for ultralow-power machine vision using photonic devices.

Original languageEnglish
Article number2106092
JournalAdvanced Science
Volume9
Issue number15
DOIs
Publication statusPublished - 25 May 2022

Keywords

  • CsAgBiBr
  • in-sensors
  • lead-free double perovskites
  • machine vision
  • reservoir

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