记忆电阻器
神经形态工程学
重置(财务)
材料科学
电阻随机存取存储器
计算机科学
切换时间
非易失性存储器
热传导
光电子学
纳米技术
电子工程
电压
电气工程
人工智能
人工神经网络
工程类
金融经济学
复合材料
经济
作者
Sungho Kim,Shinhyun Choi,Wei Lü
出处
期刊:ACS Nano
[American Chemical Society]
日期:2014-02-26
卷期号:8 (3): 2369-2376
被引量:439
摘要
Memristors have been proposed for a number of applications from nonvolatile memory to neuromorphic systems. Unlike conventional devices based solely on electron transport, memristors operate on the principle of resistive switching (RS) based on redistribution of ions. To date, a number of experimental and modeling studies have been reported to probe the RS mechanism; however, a complete physical picture that can quantitatively describe the dynamic RS behavior is still missing. Here, we present a quantitative and accurate dynamic switching model that not only fully accounts for the rich RS behaviors in memristors in a unified framework but also provides critical insight for continued device design, optimization, and applications. The proposed model reveals the roles of electric field, temperature, oxygen vacancy concentration gradient, and different material and device parameters on RS and allows accurate predictions of diverse set/reset, analog switching, and complementary RS behaviors using only material-dependent device parameters.
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