神经形态工程学
记忆电阻器
计算机科学
可靠性(半导体)
电阻随机存取存储器
过程(计算)
图层(电子)
计算机体系结构
班级(哲学)
电子工程
功率(物理)
材料科学
人工神经网络
电气工程
纳米技术
工程类
人工智能
物理
电压
量子力学
操作系统
作者
Ayesha Zaman,Eunsung Shin,Chris Yakopcic,Tarek M. Taha,Guru Subramanyam
标识
DOI:10.1109/naecon.2018.8556749
摘要
Memristor devices have the potential to drive a new class of specialized low power embedded hardware. The unique characteristics of these non-volatile and nanoscale devices allow them to perform parallel analog computing with extreme efficiency. To help facilitate the design of such systems, this paper describes the fabrication and characterization process used to develop memristors that are strong candidates for use in neuromorphic systems. In this work two different types of memristor devices, those with a GeTe switching layer, and those with a VO 2 switching layer, are characterized and analyzed. These results are used to determine device suitability for use in neuromorphic computing applications through the properties of symmetry, reliability, stability, and programmability. In short, repeatable multi-level resistive switching has been investigated and the results have been summarized.
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