Ferroelectric memory based on two-dimensional materials for neuromorphic computing. Issue 2 (1st June 2022)

• Record Type: Journal Article
• Authors: Chen, Li; Pam, Mei Er; Li, Sifan; Ang, Kah-Wee
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 2(2022)
• Publication Date: 2022-06-01
• Subjects: 2D materials -- ferroelectrics -- memory -- neuromorphic computing; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac57cb
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Ferroelectric memory devices with fast-switching speed and ultra-low power consumption have been recognized as promising building blocks for brain-like neuromorphic computing. In particular, ferroelectric memories based on 2D materials are attracting increasing research interest in recent years due to their unique properties that are unattainable in conventional materials. Specifically, the atomically thin 2D materials with tunable electronic properties coupled with the high compatibility with existing complementary metal-oxide-semiconductor technology manifests their potential for extending state-of-the-art ferroelectric memory technology into atomic-thin scale. Besides, the discovery of 2D materials with ferroelectricity shows the potential to realize functional devices with novel structures. This review will highlight the recent progress in ferroelectric memory devices based on 2D materials for neuromorphic computing. The merits of such devices and the range of 2D ferroelectrics being explored to date are reviewed and discussed, which include two- and three-terminal ferroelectric synaptic devices based on 2D materials platform. Finally, current developments and remaining challenges in achieving high-performance 2D ferroelectric synapses are discussed.