Electrochemical Preparation of Reliable and High Yield Memristors for Efficient Reservoir Computing Systems

Reservoir computing (RC) systems have a time signal processing architecture with the advantages of high efficiency and low training cost. Oxide-based memristors present a promising solution for the development of high-performance, scalable, memristive reservoir computing systems, benefiting from their inherent dynamic nonlinearity and substantial commercial potential. Compared to conventional thin-film deposition techniques, the anodization technique demonstrates advantages in cost-effectiveness, processing speed, and operational simplicity in preparing oxide films for memristors. However, anodized memristors usually have limited device structures, and their nonvolatile characteristics are incompatible with the RC systems. In this study, TiN/NbO_x/Pt memristors with low cycle temporal variation (<5%) and high yield are prepared via the anodization technique at 40 s. The resistive mechanism of memristors has been systematically investigated, and the devices have been modeled accordingly. Then, compression of MNIST images in both horizontal and vertical dimensions is achieved through memristors. Compared to the original data, the training time is reduced by 86.8% while ensuring the classification accuracy (97.25%). The memristor-based reservoir computing network exhibits good prediction of Hénon map sequences at the simulation and hardware level with an average power consumption as low as 1.97 μW for a single pulse.