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 × 10³A/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.

视觉自适应设备有潜力简化机器视觉系统中的电路和算法，以适应和感知具有不同亮度水平的图像，然而，这受到缓慢的适应过程的限制。这里，提出了仿生二维（2D）晶体管中的雪崩调谐作为前馈抑制，用于具有微秒级精确感知的快速高频视觉适应行为，适应速度比人类视网膜快10 ⁴倍以上报道了仿生传感器。随着光强度的变化，仿生晶体管自发地在雪崩效应和光电导效应之间切换，改变幅度和符号的响应度（从7.6 × 10 ⁴到−1 × 10 ³ A/W），从而实现超快的暗视和明视适应过程分别为 108 和 268 μs。通过进一步将卷积神经网络与雪崩调谐仿生晶体管相结合，实现了自适应机器视觉，具有卓越的微秒级快速适应能力和强大的图像识别能力，在昏暗和明亮的条件下精度均超过 98%。