A review of MXene memristors and their applications

Abstract

The rapid growth of artificial intelligence (AI) demands efficient management of vast data quantities, a challenge that traditional von Neumann computing struggles to meet due to its power consumption and memory limitations. Memristive devices have emerged as a promising solution to overcome the von Neumann bottleneck through in-memory computing, which is crucial for neuromorphic computing advancements. Among the various materials investigated for memristor development, MXenes have recently gained attention as a highly promising platform. These materials exhibit a wide range of functional behaviors due to their unique electrochemical properties. MXenes offer several advantages, including high electrical conductivity, tunable surface chemistry, and excellent mechanical flexibility, enhancing their potential in advancing memristor technology. This review begins by introducing various MXene-based devices and highlighting switching mechanisms. It then explores the application of MXene memristors in neuromorphic and logic operations. The review concludes by addressing the challenges associated with MXene memristors, examining the obstacles they present, and considering future prospects in this dynamic field.