Ti3C2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti3C2 MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti3C2 MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti3C2 MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti3C2/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti3C2/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti3C2 MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti3C2 MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.

Automated summary

MXene, a novel 2D nanomaterial with excellent electrical, mechanical, and chemical properties, shows promising potential in digital and analog computing applications, especially in bipolar resistive switching and synaptic learning devices. The study explored Ti3C2 MXene and investigated its structural, compositional, and morphological properties, as well as the impact of different top electrodes on device performance. The results indicate that MXene is a promising nanomaterial for non-volatile memory and synaptic learning applications.