记忆电阻器
自旋电子学
可扩展性
计算机科学
量子计算机
电气工程
量子
工程类
物理
量子力学
数据库
铁磁性
作者
Jiajia Qin,Bai Sun,Guangdong Zhou,Tao Guo,Yuanzheng Chen,Chuan Ke,Shuangsuo Mao,Xiaoliang Chen,Jinyou Shao,Yong Zhao
出处
期刊:ACS materials letters
[American Chemical Society]
日期:2023-07-19
卷期号:5 (8): 2197-2215
被引量:6
标识
DOI:10.1021/acsmaterialslett.3c00088
摘要
The high-speed development of the Internet of Things and artificial intelligence is revolutionizing the world in terms of industrial production, environmental protection, medical treatment, education, daily life, and so on. The powerful and fast computing methods are crucial for the advanced computing technology toward the next generation artificial intelligence. Traditional computing systems have separated logical and storage units, which cause computation time delays and increase power consumption. Spintronic memristors combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, which can meet the high-speed, low-power, and scalability requirements of quantum computing (QC) for quantitative information processing. This paper reviews the research progress of spintronic memristors based on magnetic tunnel junction (MTJ), domain wall (DW) motion, and spin wave (SW), respectively, focusing on the development and challenges of spintronic memristors for QC. Finally, some problems that need to be solved urgently in the current research are summarized, and the potential applications of spintronic memristors are discussed.
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