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

Ling Li, Shasha Li, Wenhai Wang, Jielian Zhang, Yiming Sun, Qunrui Deng, Tao Zheng, Jianting Lu, Wei Gao, Mengmeng Yang, Hanyu Wang, Yuan Pan, Xueting Liu, Yani Yang, Jingbo Li and Nengjie Huo ()
Additional contact information
Ling Li: South China Normal University
Shasha Li: Chaohu University
Wenhai Wang: South China Normal University
Jielian Zhang: South China Normal University
Yiming Sun: South China Normal University
Qunrui Deng: South China Normal University
Tao Zheng: South China Normal University
Jianting Lu: China Electronic Product Reliability and Environmental Testing Research Institute
Wei Gao: South China Normal University
Mengmeng Yang: South China Normal University
Hanyu Wang: South China Normal University
Yuan Pan: South China Normal University
Xueting Liu: South China Normal University
Yani Yang: South China Normal University
Jingbo Li: College of Optical Science and Engineering
Nengjie Huo: South China Normal University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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 104 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 × 104 to −1 × 103 A/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.

Date: 2024
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DOI: 10.1038/s41467-024-50488-6

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