Spatiotemporal Reservoir Computing with a Reconfigurable Multifunctional Memristor Array

Abstract

Abstract The existing physical implementations of reservoir computing are constrained mainly by time‐delay architectures that lack capabilities for spatial data processing. This study presents a multifunctional memristor‐based reservoir computing system, the memristive echo state network (MESN), which enables spatiotemporal computation within a single device crossbar array. Utilizing a reconfigurable Ta/HfO 2 /RuO 2 memristor, three distinct switching modes are realized: stochastic for input masking, bistable for sigmoidal activation, and analog for precise readout. A full in‐memory implementation is experimentally demonstrated using a one‐transistor‐one‐resistor crossbar array integrated with indium oxide thin‐film transistors. Spatial inference is validated through cellular automata, confirming reliable hardware operation. High‐level simulations based on the hardware results demonstrate the performance of the proposed MESN, achieving high accuracy in predicting the Lorenz attractor and classifying attention‐deficit/hyperactivity disorder. The system also predicted the Kuramoto–Sivashinsky equation, representing the first memristor‐based reservoir to model complex spatiotemporal partial differential equations. These results highlight the potential of multifunctional memristor arrays for scalable in‐memory spatiotemporal computing.