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

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 exhibits spontaneously switches between a linear and photoconductive effect, varying responsivity in both magnitude and gain (from 7.6 × 10⁻⁴ to -1 × 10⁻³ A/W), thereby achieving ultra-fast adaptive and photopic adaptation process at 268 µs, respectively. By combining convolutional neural networks with avalanche-driven bionic transistor, an adaptive machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both and bright conditions.

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, Nengjie Huo