MXenes: promising 2D memristor materials for neuromorphic computing components

Abstract

Brain-inspired parallel computing ‘neuromorphic computing’ is one of the most promising technologies for efficiently handling large amounts of information data, which operates based on a hardware-neural network platform consisting of numerous artificial synapses and neurons. Memristors, as artificial synapses based on various 2D materials for neuromorphic and data storage technologies with low power consumption, high scalability, and high speed, have been developed to address the von Neumann bottleneck and limitations of Moore's law. The 2D MXenes have strong potential application in memristors due to their ultrahigh conductivity, fast charge response, high stacking density, and high hydrophilicity. Here, we discuss how MXenes are emerging as a potential material towards artificial synapses. Recent progress in research on artificial synapses, fabricated particularly using MXenes and their composite materials, is comprehensively discussed with respect to mechanism, synaptic characteristics, power efficiency, and scalability. Finally, we present an outlook of the future development of MXenes for artificial intelligence and challenges in integrating memristors with MXenes are briefly discussed. © 2022 Elsevier Inc.