2D materials-based crossbar array for neuromorphic computing hardware

Abstract

Abstract The growing demand for artificial intelligence has faced challenges for traditional computing architectures. As a result, neuromorphic computing systems have emerged as possible candidates for next-generation computing systems. Two-dimensional (2D) materials-based neuromorphic devices that emulate biological synapses and neurons play a key role in neuromorphic computing hardware due to their unique properties such as high strength, thermal conductivity, and flexibility. Although several studies have shown the simulations of individual devices, experimental implementation of large-scale crossbar arrays is still unclear. In this review, we explore the working principles and mechanisms of memristive devices. Then, we overview the development of neuromorphic devices based on 2D materials including transition metal dichalcogenides, graphene, hexagonal boron nitride, and layered halide perovskites. We also highlight the requirement and recent progress for building crossbar arrays by utilizing the advantageous properties of 2D materials. Lastly, we address the challenges that hardware implementation of neuromorphic computing systems currently face and propose a path towards system-level applications of neuromorphic computing.