神经形态工程学
记忆电阻器
离子
材料科学
能量(信号处理)
计算机科学
纳米技术
离子键合
电导
人工神经网络
光电子学
电子工程
物理
工程类
人工智能
凝聚态物理
量子力学
作者
Yujie Sun,Rongjie Zhang,Changjiu Teng,Junyang Tan,Zehao Zhang,Shengnan Li,Jingwei Wang,Shilong Zhao,Wenjun Chen,Bilu Liu,Hui–Ming Cheng
标识
DOI:10.1016/j.mattod.2023.04.013
摘要
Memristor-based neuromorphic computing is promising for artificial intelligence. However, most of the reported memristors have limited linear computing states and consume large operation energy which hinder their applications. Herein, we report a memristor based on ionic two-dimensional CuInP2S6 (2D CIPS), in which up to 1350 linear conductance states are achieved by controlling the migration of internal Cu ions in CIPS. In addition, the device shows a low operation current of ∼100 pA. Cu ions are proven to move along the electric field by in-situ scanning electron microscopy and energy dispersive spectroscopy measurements. Furthermore, complex signal transport among multiple neurons in the brain is imitated by 2D CIPS-based memristor arrays. Our results offer a new platform to fabricate high-performance memristors based on ion transport in 2D materials for neuromorphic computing.
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