Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film

IF 6 Q1 ENGINEERING, MULTIDISCIPLINARY Results in Engineering Pub Date : 2024-09-16 DOI:10.1016/j.rineng.2024.102906
Se Yeon Jeong , Jaeho Jung , Hyun Kyu Seo , Jae-Seung Jeong , June Hyuk Lee , Gun Hwan Kim , Min Kyu Yang
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Abstract

With the accelerated development of artificial intelligence-oriented hardware components, research on low-power, high-density memory devices is actively being conducted. Among various memory devices, resistive switching devices with crossbar structures have been extensively researched owing to their many advantages. To address the sneak current issue that is inherent in memory devices with crossbar structures, additional selection devices have been considered. However, self-rectifying resistive switching devices are known to be advantageous for harnessing structural benefits. Although significant research has been conducted in this area and remarkable results have been published, further research is required to improve the electrical characteristics for low-power, high-density memory applications This paper introduces self-rectifying devices with low power consumption, high rectification ratios, and high reliability. By combining HfZrOx resistance-change layers and SiOx interlayers, the characteristics of self-rectifying devices were confirmed, achieving a rectification ratio of 106 and 100 % operational yield in 1 kb crossbar devices. The essential multiply-and-accumulate operations in artificial intelligence-oriented hardware components were verified, and the applicability of the device as an artificial neural network was explored through simulations.
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使用氧化锆铪薄膜的高性能自矫正记忆器件功能界面层
随着面向人工智能的硬件元件的加速发展,人们正在积极开展低功耗、高密度存储器件的研究。在各种存储器件中,具有横条结构的电阻开关器件因其诸多优点而被广泛研究。为了解决交叉条结构存储器件固有的潜电流问题,人们考虑了其他选择器件。然而,众所周知,自整流电阻开关器件在利用结构优势方面具有优势。虽然在这一领域已经开展了大量研究,并发表了令人瞩目的成果,但仍需进一步研究,以改善低功耗、高密度存储器应用的电气特性。 本文介绍了具有低功耗、高整流比和高可靠性的自整流器件。通过结合 HfZrOx 电阻变化层和 SiOx 中间层,证实了自整流器件的特性,在 1 kb 交叉条器件中实现了 106 的整流比和 100 % 的工作良率。在面向人工智能的硬件元件中,验证了基本的乘法和累加操作,并通过模拟探索了该器件作为人工神经网络的适用性。
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来源期刊
Results in Engineering
Results in Engineering Engineering-Engineering (all)
CiteScore
5.80
自引率
34.00%
发文量
441
审稿时长
47 days
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