光电二极管
材料科学
编码(社会科学)
光电子学
聚合物
计算机科学
复合材料
统计
数学
作者
Xiang Wan,Jie Yan,Runfeng Wang,Kunfang Chen,Tingting Ji,Xin Chen,Lijian Chen,Li Zhu,Dongyoon Khim,Zhihao Yu,Liuyang Sun,Huabin Sun,Chee Leong Tan,Yong Xu
标识
DOI:10.1021/acs.jpclett.4c02571
摘要
The integration of optoelectronic devices with reservoir computing offers a novel and effective approach to in-sensor computing. This work presents a hybrid digital-physical solution that leverages the high-performance poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)-3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) organic polymer-based photodiodes for the hardware implementation of reservoir computing system. The photodiodes demonstrate nonlinear photoelectric responses, fading memory, and cyclical stability, in relation to the temporal information on light stimuli. These attributes enable effective mapping, historical context sensitivity, and consistent performance, with time-encoded inputs, which are particularly essential for accurate and continuous processing of time series data. The optoelectronic reservoir computing system with pulse width modulation (PWM) coding demonstrates impressive performance in the prediction of chaotic sequences, achieving a normalized root-mean-square error as low as 0.095 with optimized parameters. The DPPT-TT-based photodiodes and time-based coding offer a hardware-efficient solution for reservoir computing, significantly advancing Internet of Things applications.
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