神经形态工程学
材料科学
电阻随机存取存储器
纳米线
电阻式触摸屏
硅纳米线
光电子学
硅
纳米技术
计算机科学
电气工程
工程类
人工神经网络
人工智能
电压
作者
Won Joo Lee,Boram Kim,Minsuk Koo,Yoon Kim
标识
DOI:10.1021/acsaelm.3c01680
摘要
Resistive random-access memory (RRAM) has garnered attention as a synaptic device for neuromorphic systems. However, RRAM typically suffers from various issues, such as a high-forming voltage and significant variation in switching behaviors. To address these, we propose three-dimensional-stacked RRAM based on stacked double-tip Si nanowires. Sharp-tipped Si electrodes reduce the switching voltage through the field concentration effect and minimize cycle-to-cycle variation by effectively controlling the location of conductive filament formation. Additionally, our analysis explored how these benefits enhance the accuracy of neuromorphic systems. In pattern recognition tasks using the Modified National Institute of Standards and Technology database, we achieved an accuracy of 85%, which is 47% higher compared with that of devices that do not utilize the double-tip structure.
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