Udaya Mohanan Kannan
Department of Electronic Engineering, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.
Nanomaterials (Basel). 2024 Mar 15;14(6):527. doi: 10.3390/nano14060527.
Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate neuronal behaviors. RRAM devices excel in terms of their compact size, fast switching capabilities, high ON/OFF ratio, and low energy consumption, among other advantages. This review focuses on the multifaceted aspects of RRAM devices and their application to brain-inspired computing. The review begins with a brief overview of the essential biological concepts that inspire the development of bio-mimetic computing architectures. It then discusses the various types of resistive switching behaviors observed in RRAM devices and the detailed physical mechanisms underlying their operation. Next, a comprehensive discussion on the diverse material choices adapted in recent literature has been carried out, with special emphasis on the benchmark results from recent research literature. Further, the review provides a holistic analysis of the emerging trends in neuromorphic applications, highlighting the state-of-the-art results utilizing RRAM devices. Commercial chip-level applications are given special emphasis in identifying some of the salient research results. Finally, the current challenges and future outlook of RRAM-based devices for neuromorphic research have been summarized. Thus, this review provides valuable understanding along with critical insights and up-to-date information on the latest findings from the field of resistive switching devices towards brain-inspired computing.
神经形态计算已成为一种替代计算范式，以满足数据密集型应用不断增长的计算需求。在这种背景下，电阻式随机存取存储器（RRAM）器件因其能够模拟复杂的神经元行为而在神经形态研究界引起了极大的兴趣。RRAM器件在尺寸紧凑、开关速度快、开/关比高和能耗低等方面表现出色。本综述重点关注RRAM器件的多方面特性及其在受脑启发计算中的应用。综述首先简要概述了启发仿生计算架构发展的基本生物学概念。然后讨论了在RRAM器件中观察到的各种电阻开关行为及其操作背后的详细物理机制。接下来，对近期文献中采用的各种材料选择进行了全面讨论，特别强调了近期研究文献中的基准结果。此外，综述对神经形态应用中的新兴趋势进行了全面分析，突出了利用RRAM器件取得的最新成果。在确定一些显著的研究成果时，特别强调了商业芯片级应用。最后，总结了基于RRAM的器件在神经形态研究中的当前挑战和未来展望。因此，本综述提供了有价值的理解，以及关于电阻开关器件领域朝着受脑启发计算方向的最新发现的关键见解和最新信息。
