Temporal dynamics of TiO <i> _x </i> memristor for reservoir computing applications

Abstract The growing computational demands of artificial intelligence have accelerated the development of energy-efficient neuromorphic systems capable of processing spatiotemporal information. Reservoir computing (RC) offers a promising approach with low training complexity, particularly when implemented using emerging devices such as memristors. In this work, we present a memristor-based RC system employing vertically stacked Pt/TiO x /Au volatile memristors that inherently exhibit short-term plasticity. These devices enable temporal information encoding via pulse-driven modulation and natural relaxation. Through a modified MNIST classification task, we demonstrate that the system performance deteriorates significantly with delayed readout and small levels of device variation, highlighting the need for robust timing strategies. A virtual memristor model was also developed to evaluate system performance on the Mackey-Glass chaotic time-series forecasting task, achieving up to 93.6% prediction accuracy by tuning the internal time constant. These findings highlight the importance of precise readout control and variation resilience in the design of practical memristor-based RC systems for real-world neuromorphic applications.