Temporal Adaptivity Enabled High‐Efficiency In‐Sensor Reservoir Computing Based on MoS2 Phototransistors
Xinlong Zeng, Siyuan Xu, Xiangwei Su, Cheng Zhang, Tianjiao Zhang, Hongzhao Wu, Yan Ling Wu, Yiqi Chen, Yang Xu, Bin Yu, Yang Liu, Yunfan Guo, Xiang Guo, Wei Xu, Yuda Zhao · Small · 2025
Abstract Reservoir computing (RC) excels in temporal signal processing, driving advances in efficient reservoir hardware. However, dynamic target recognition faces challenges due to mismatches between event time scales and temporal properties of the optoelectronic RC system. In this work, a bridge is built between the event chronological information and the temporal dynamic of optoelectronic physical nodes in RC. The optoelectronic physical nodes are fabricated based on MoS2 phototransistors with varied fading memory timescale (τ) as the in‐sensor RC hardware. Then the matching of τ with the time interval (Δt) of the input stimulus is explored by evaluating the linear separability (R2) of reservoir states. When Δt/τ is within the range of 10–20%, the 32 output reservoir states from a 5‐bit optical input display excellent linear separability with the R2 of 0.988 ± 0.006, contributing to the high accuracy rate of > 85.2% in recognizing eight sets of gestures. In comparison, when Δt/τ is out of the range of 10–20%, the R2 decreases and the recognition rate is below 77.6%. This study systematically quantifies the critical relationship between temporal scaling parameters and the time interval of optical input, providing a method to design the temporally adaptive optoelectronic physical nodes for high‐efficiency in‐sensor RC systems.