2D Ti3C2Tx MXene-derived self-assembled 3D TiO2nanoflowers for nonvolatile memory and synaptic learning applications

Abstract

Two-dimensional (2D) semiconducting materials and transition-metal oxides are promising materials for nonvolatile memory and brain-inspired neuromorphic computing applications. However, it remains challenging to obtain high-quality stacked 2D films with low energy consumptions (or drive currents) because of their high interfacial resistance. In this study, we synthesized 2D Ti3C2Tx MXene-derived three-dimensional (3D) TiO2 nanoflowers (NFs) as a feasible resistive switching (RS) material with outstanding electronic properties and synaptic learning capabilities. The electrical and optical characteristics of the synthesized material were determined through density functional theory calculations. Electrical measurements of the Al/Ti3C2Tx-TiO2 NF/Pt memory device indicated the occurrence of forming-free switching phenomena with extremely low switching voltages (0.68–0.53 V), stable ON/OFF ratio (2.3 × 103), and retention greater than 105 s. The Holt–Winters exponential smoothing technique was used for modeling and predicting the switching voltages of the RS device. The mechanism underlying the reliable RS was confirmed by observing the dense conductive filaments through conductive atomic force microscopy. Interestingly, the 2D Ti3C2Tx MXene-derived 3D TiO2 NF-based RS device mimicked the potentiation/depression and spike-time-dependent plasticity of a biological synapse. Finally, a convolutional neural network was implemented based on the observed synaptic weights of Al/Ti3C2Tx-TiO2 NF/Pt for image-edge detection.