Zhou Hangbo, Li Sifan, Ang Kah-Wee, Zhang Yong-Wei
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore.
Nanomicro Lett. 2024 Feb 19;16(1):121. doi: 10.1007/s40820-024-01335-2.
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
传统计算架构面临着诸多重大挑战,包括内存与处理单元之间的高延迟和高能耗。作为应对措施,内存计算已成为一种颇具前景的替代架构,它能够在内存阵列中执行计算操作,从而克服这些限制。忆阻器件因其高密度阵列、快速响应时间以及模拟生物突触的能力,作为内存计算的关键组件而备受关注。在这些器件中,基于二维(2D)材料的忆阻器和忆晶体管阵列已成为下一代内存计算特别有前景的候选者,这得益于二维材料的独特特性(如层状结构、机械柔韧性以及形成异质结的能力)所驱动的卓越性能。本综述深入探讨了基于二维材料的忆阻阵列的最新研究,涵盖材料选择、器件性能指标、阵列结构和潜在应用等关键方面。此外,它还全面概述了与这些阵列相关的当前挑战和限制以及潜在的解决方案。本综述的主要目标是成为利用二维材料实现下一代内存计算的一个重要里程碑,并弥合从单器件表征到神经形态计算的阵列级和系统级实现之间的差距,充分利用基于二维材料的忆阻器件的潜力。