从氧化物到二维材料:为高能效神经形态计算推进忆阻器技术的发展

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Moon-Seok Kim,  and , Sungho Kim*, 
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引用次数: 0

摘要

本综述对基于氧化物和二维(2D)材料的忆阻器的模拟开关性能进行了比较分析,重点关注它们在神经形态计算系统中的应用。本研究考察了各种性能指标,如耐用性、能耗和开关特性,以阐明这些参数如何受到各自材料独特特性的影响。通过研究阵列配置中基于氧化物和二维材料的忆阻器,本综述深入探讨了它们在神经形态计算中的适用性,重点介绍了忆阻器技术的进步、挑战和未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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