新兴材料中的电阻转换及其在神经形态计算中的特性

Mohd Asif , Ashok Kumar
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引用次数: 11

摘要

在大数据存储时代,电阻型随机存取存储器具有成本低、运行速度快、功耗低、耐久性和保持性高等高效特性,将成为微电子技术的重要组成部分。在这篇综述中,我们重点研究了导致电阻开关的三种物理机制:电化学金属化、价态变化机制和铁电极化。已经对这些物理机制如何在用于基于非易失性随机存取存储器元件(如氧化物、铁电体、硫族化物、聚合物、石墨烯基电阻开关等)的电阻开关的各种材料中工作进行了详细讨论。还讨论了神经形态计算中模拟电阻开关的理想电学和光学特性。一份广泛的报告研究了不同材料对人造忆阻器的器件要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Resistive switching in emerging materials and their characteristics for neuromorphic computing

Resistive switching in emerging materials and their characteristics for neuromorphic computing

Resistive random access memory would be an important component of microelectronics in the era of big data storage due to its efficient characteristics such as low cost, fast operating speed, low power consumption, and high performance in respect of endurance and retention. In this present review, we have focused on surveying three physical mechanisms which lead to resistive switching: electrochemical metallization, valence change mechanism, and ferroelectric polarization. A detailed discussion has been carried out on how these physical mechanisms work in various materials used for resistive switching based on nonvolatile random access memory elements such as oxides, ferroelectric, chalcogenides, polymers, graphene-based resistive switching, etc. The desirable electrical and optical properties for the representation of analog resistive switching in neuromorphic computing have also been discussed. An extensive report has examined the device requirement of different materials for artificial memristors.

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