Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?
Xiwen Liu, Keshava Katti, and Deep Jariwala

TL;DR
This paper explores the potential of 2D materials to advance neuromorphic hardware by leveraging their unique properties for non-volatile memory devices, highlighting recent progress and remaining challenges.
Contribution
It reviews the state-of-the-art in 2D material-based neuromorphic devices and discusses the key challenges in scalable synthesis and device performance.
Findings
2D materials enable diverse non-volatile memory applications.
Atomically-thin structures offer performance advantages in neuromorphic devices.
Device variability remains a significant challenge for practical applications.
Abstract
Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der Waals structures that enable ease of integration with conventional electronic materials and silicon-based hardware. All major classes of non-volatile memory (NVM) devices have been demonstrated using 2D materials, including their operation as synaptic devices for applications in neuromorphic computing hardware. Their atomically-thin structure, superior physical properties, i.e., mechanical strength, electrical and thermal conductivity, as well as gate-tunable electronic properties provide performance advantages and novel functionality in NVM devices and systems. However, device performance and variability as compared to incumbent materials and…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Machine Learning in Materials Science
