非易失性存储器
材料科学
记忆电阻器
电介质
神经形态工程学
光电子学
成核
阈值电压
图层(电子)
电压
纳米技术
电气工程
晶体管
化学
计算机科学
工程类
机器学习
人工神经网络
有机化学
作者
Xiangxiang Ding,Peng Huang,Yudi Zhao,Yulin Feng,Lifeng Liu
标识
DOI:10.1109/ted.2022.3144373
摘要
Memristors, possessing volatile and nonvolatile switching characteristics, exhibit great potential for high-density data storage and neuromorphic computing system. However, the adjustment of device electrical characteristics, such as drive current and threshold voltage, is still not specific, which hinders the optimization of memristors for desired applications. In this work, we fabricated Ag-based memristive devices with different stoichiometries and density of silicon suboxide (SiO x ) dielectric layers, which can both exhibit volatile and nonvolatile switching behaviors and the volatile and nonvolatile switching can transfer each other by varying the compliance current. The experimental results showed that high stoichiometry of SiO x film will lead to high compliance current for the transition from volatile to nonvolatile characteristics in Ag-based memristors. A model based on classical nucleation theory (CNT) was proposed to explain the transition current of different samples. It was found that the volatile and nonvolatile switching behaviors had a close correlation with the surface energy of conductive filament and defects in the dielectric layer. Besides, the dielectric layer of memristors deposited under low RF power exhibited large density, which caused large threshold voltage during the set process. This work provides the adjustment methods of drive current and threshold voltage from material properties.
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