神经形态工程学
电阻随机存取存储器
记忆电阻器
冯·诺依曼建筑
双层
计算机科学
材料科学
计算机数据存储
峰值时间相关塑性
纳米技术
电子工程
光电子学
人工智能
突触可塑性
人工神经网络
计算机硬件
电压
工程类
电气工程
化学
操作系统
生物化学
受体
膜
作者
Qingda Meng,Tianquan Fu,Huadong Yang,Ye Tao,Chunran Li,Xiao-Ming Xiu
标识
DOI:10.1088/1361-6641/ac3cc7
摘要
Abstract Human brain synaptic memory simulation based on resistive random access memory (RRAM) has enormous potential to replace the traditional von Neumann digital computer thanks to several advantages, including its simple structure, its high-density integration, and its capabilities regarding information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering the AlO x /HfO x bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitating several synaptic learning functions, including learning experience behaviors, short-term plasticity, long-term plasticity transition, and spike-timing-dependent plasticity (STDP). In addition, the memristor based on STDP learning rules is implemented in image pattern recognition. These results may show the potential of HfO x -based memristors for future information storage and neuromorphic computing applications.
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