Bioinspired In‐Sensor Reservoir Computing for Self‐Adaptive Visual Recognition with Two‐Dimensional Dual‐Mode Phototransistors

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.