In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective

Abstract

The reservoir computing (RC) system, known for its ability to seamlessly integrate memory and computing functions, is considered as a promising solution to meet the high demands for time and energy-efficient computing in the current big data landscape, compared with traditional silicon-based computing systems that have a noticeable disadvantage of separate storage and computation. This review focuses on in-materio RC based on nanowire networks (NWs) from the perspective of materials, extending to reservoir devices and applications. The common methods used in preparing nanowires-based reservoirs, including the synthesis of nanowires and the construction of networks, are firstly systematically summarized. The physical principles of memristive and memcapacitive junctions are then explained. Afterwards, the dynamic characteristics of nanowires-based reservoirs and their computing capability, as well as the neuromorphic applications of NWs-based RC systems in recognition, classification, and forecasting tasks, are explicated in detail. Lastly, the current challenges and future opportunities facing NWs-based RC are highlighted, aiming to provide guidance for further research.