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 Ti₃C₂T_x 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/Ti₃C₂T_x-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 Ti₃C₂T_x 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/Ti₃C₂T_x-TiO₂ NF/Pt for image-edge detection.

Key words: Mxene, Ti₃C₂T_x-TiO₂ nanoflowers, Resistive switching, Synaptic learning, Density functional theory, Time-series analysis