School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P.R. China.
School of Electronic Engineering, Chaohu University, Hefei, 238000, China.
Nat Commun. 2024 Jul 24;15(1):6261. doi: 10.1038/s41467-024-50488-6.
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.
视觉自适应器件具有简化机器视觉系统电路和算法的潜力，可适应和感知具有不同亮度水平的图像，但这受到缓慢的自适应过程的限制。在这里，提出了在仿生二维 (2D) 晶体管中作为前馈抑制的雪崩调谐，以实现具有微秒级精确感知的快速和高频视觉自适应行为，其自适应速度比人类视网膜和报道的仿生传感器快 10 多倍。随着光强的变化，仿生晶体管在雪崩和光电导效应之间自发切换，响应度的幅度和符号都发生变化（从 7.6×10 到-1×10A/W），从而分别实现超快暗视觉和明视觉的自适应过程，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，具有显著的微秒级快速自适应能力，在昏暗和明亮条件下均具有超过 98%的高精度图像识别能力。
