神经形态工程学
记忆电阻器
卤化物
钙钛矿(结构)
偏压
材料科学
光伏系统
光电子学
逻辑门
纳米技术
探测器
联轴节(管道)
计算机科学
电压
电子线路
计算机体系结构
功率(物理)
非常规计算
集成电路
人工神经网络
光电探测器
电阻随机存取存储器
和大门
数字电子学
油藏计算
互连
电气工程
电子工程
作者
Dongsheng Cui,Pusheng Guo,Yumeng Xu,Xiangxiang Gao,Xing Guo,Wei Wei,Zhenhua Lin,Jincheng Zhang,Yue Hao,Jingjing Chang
出处
期刊:Nano Letters
[American Chemical Society]
日期:2025-10-16
卷期号:25 (43): 15705-15713
被引量:7
标识
DOI:10.1021/acs.nanolett.5c04297
摘要
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.
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