Self-Powered Halide Perovskite Optoelectronic Synaptic Memristors for Reconfigurable Logic and Reservoir Computing Applications

作者：Dongsheng Cui, Pusheng Guo, Yumeng Xu, Xiangxiang Gao, Xing Guo, Wei Wei, Zhenhua Lin, Jincheng Zhang, Yue Hao, Jingjing Chang · 发表于：Nano Letters · 年份：2025 · DOI：10.1021/acs.nanolett.5c04297 · 被引用次数：14 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Reservoir Computing、Perovskite Materials and Applications

The development of low-power neuromorphic systems requires the integration of sensing, logic operations, neuromorphic computing, and energy autonomy. Herein, we present a triple-cation and triple-anion perovskite-based (p-type/intrinsic/n-type) p-i-n optoelectronic memristor array that synergistically combines these functions. The device's inherent photovoltaic effect (∼0.8 V) enables self-powered optical synaptic plasticity at 520 nm, eliminating external biasing for near-zero power consumption. By coupling this intrinsic photovoltaic bias with tunable external voltages, we demonstrate four reconfigurable Boolean logic operations (NOT, XOR, NAND, IMPLY). Furthermore, a reservoir computing (RC) system for neuromorphic pattern recognition is implemented by leveraging the plasticity of the perovskite memristor, achieving classification accuracies of 97.94% for 1-bit and 90.73% for 4-bit handwritten digit recognition. The self-powered memristor integrating optical synapses, digital logic, and neuromorphic functionalities provide new paradigms for developing next-generation low-power, high-density integrated circuits with hybrid digital-analog architectures.