记忆电阻器
神经形态工程学
材料科学
钙钛矿(结构)
纳米技术
冯·诺依曼建筑
计算机科学
电阻随机存取存储器
光电子学
电子工程
人工神经网络
电压
人工智能
电气工程
工程类
化学工程
操作系统
作者
Zehan Liu,Pengpeng Cheng,Ruyan Kang,Jian Zhou,Xiaoshan Wang,Xian Zhao,Jia Zhao,Zhiyuan Zuo
标识
DOI:10.1021/acsanm.3c04006
摘要
Neuromorphic computing systems integrating both analog computing and data storage at the hardware level fundamentally overcome the von Neumann bottleneck. Organolead halide perovskite memristors are considered to be promising candidates to mimic biological synapses due to their highly tunable resistance states. Despite the considerable progress already made, the toxicity and instability of lead severely restrict potential applications. Herein, we successfully demonstrate all-inorganic and lead-free nanoscale Cs 3 Bi 2 X 9 perovskites films. The Cs 3 Bi 2 I 9 -based memristors exhibited an excellent resistive switching (RS) performance, including ultralow power consumption, high environmental robustness, ultrafast switching speed, stable endurance and retention, large ON/OFF ratio, and multilevel capability. In particular, we propose an electrothermal simulation model and combine various experimental approaches to analyze the RS mechanism. More importantly, essential synaptic functions are emulated by Cs 3 Bi 2 I 9 -based memristors, including paired-pulse facilitation, long-term potentiation/depression, a learning–forgetting–relearning process, spike-voltage-dependent plasticity, spike-frequency-dependent plasticity, and so on. Basic photonic synaptic function plasticity was also investigated in the Cs 3 Bi 2 I 9 memristors with tunable photophysical properties. Overall, this work is the original systematic study of memristors based on Cs 3 Bi 2 X 9 perovskite systems and the comprehensive applications of Cs 3 Bi 2 I 9 memristors in artificial neural networks, opening opportunities for ecofriendly, hardware-oriented neuromorphic computing architectures with perovskites as the functional material.
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