Adaptative machine vision with microsecond-level accurate perception beyond human retina.

Journal: Nature communications
Published Date: Jul 24, 2024

Abstract

Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as feedforward inhibition in bionic two-dimensional (2D) transistor is proposed for fast and high-frequency visual adaptation behavior with microsecond-level accurate perception, the adaptation speed is over 10 times faster than that of human retina and reported bionic sensors. As light intensity changes, the bionic transistor spontaneously switches between avalanche and photoconductive effect, varying responsivity in both magnitude and sign (from 7.6 × 10 to -1 × 10A/W), thereby achieving ultra-fast scotopic and photopic adaptation process of 108 and 268 μs, respectively. By further combining convolutional neural networks with avalanche-tuned bionic transistor, an adaptative machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both dim and bright conditions.

Authors

  • Ling Li
    College of Communication Engineering, Jilin University, Changchun, Jilin China.
  • Shasha Li
  • Wenhai Wang
    School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
  • Jielian Zhang
    School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
  • Yiming Sun
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Qunrui Deng
    School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
  • Tao Zheng
    Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China. Electronic address: zhengtao@ms.giec.ac.cn.
  • Jianting Lu
    National Key Laboratory of Science and Technology on Reliability Physics and Application of Electronic Component, China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, 510610, China.
  • Wei Gao
    Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
  • Mengmeng Yang
    The First Medical Centre, Chinese PLA General Hospital, Beijing, China.
  • Hanyu Wang
    School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
  • Yuan Pan
    Guangdong Provincial Key Laboratory of Chip and Integration Technology, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, People's Republic of China.
  • Xueting Liu
    Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Yani Yang
    School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
  • Jingbo Li
    School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
  • Nengjie Huo
    Guangdong Provincial Key Laboratory of Chip and Integration Technology, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, People's Republic of China.

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