记忆电阻器
记忆晶体管
计算机科学
瓶颈
终端(电信)
调制(音乐)
电导
电子工程
材料科学
光电子学
人工智能
电阻随机存取存储器
电气工程
嵌入式系统
物理
工程类
电信
电压
声学
凝聚态物理
作者
Wenshuai Feng,Qingjiao Huang,Jinxia Hu,Rui‐Tao Wen
摘要
Memristors, characterized by in-memory computing and low-power consumption, are considered an ideal paradigm for building artificial neural networks and overcoming the von Neumann bottleneck. The two-terminal Li+-based memristor features simple structure and controllable weight update. However, existing works normally focus on the exclusive resistive switching layer, which is commonly the Li-source layer, and ignore the effect of another variable layer. In this study, a synchronous conductance modulation approach is developed by coupling the synchronously modulated layers of TT-Nb2O5 and LiCoO2 in the device. The linearity of the device was measured at 0.29, leading to a high recognition accuracy, with an average image recognition rate of 95.8% and a low standard deviation of 1.7%. This work offers an alternative option for developing two-terminal memristors.
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