Bioinspired In‐Sensor Reservoir Computing for Self‐Adaptive Visual Recognition with Two‐Dimensional Dual‐Mode Phototransistors
Nanjia Jiang, Jian Tang, Woyu Zhang, Yi Li, Na Li, Xiuzhen Li, Xi Chen, Renrui Fang, Zeyu Guo, Fei Wang, Jun Wang, Zhi Li, Congli He, Guangyu Zhang, Zhongrui Wang, Dashan Shang
Abstract
Abstract Artificial visual systems that dynamically process spatiotemporal optoelectronic signals under complex real‐life environments bear a wide spectrum of edge applications. Despite significant progress in optoelectronic sensors and neuromorphic computing algorithms, developing visual systems that can adapt to a broad illumination range while retaining high performance, high efficiency, and low training costs remains a challenge. Here, this work reports a bioinspired in‐sensor reservoir computing (RC) for self‐adaptive visual recognition. By leveraging voltage‐tunable photoresponses of the MoS 2 ‐based phototransistor array, the RC system demonstrates both scotopic and photopic adaptation functions and maintains a recognition accuracy of 91%. The horizontal modulation (HM) block enables the reservoir to adapt automatically in real‐time under changing illumination conditions, yielding a 90.64% recognition accuracy (14.21% improvement over conventional RC systems). These results pave the way for the emergence of a reconfigurable in‐sensor RC system with broad applications and enhanced performance for an efficient artificial vision system at the edge.