Physical reservoir computing using vertically aligned graphene/diamond photomemristors
Yuga Ito, Haruki Iwane, Siyu Jia, Kenji Ueda · Applied Physics Express · 2023
Abstract Reservoir computing is one of the most promising machine learning architectures and could allow highly efficient, high-speed processing of time-series data. Physical reservoir computing based on various physical phenomena that exhibit complicated dynamics has been widely investigated in recent years. The present work demonstrates vertically aligned graphene/diamond junctions (photomemristors) could be employed for physical reservoir computing involving image recognition of single digits. Exceptional image recognition performance of 92% was obtained due to their complex photoconducting behaviors. This work is expected to assist in the realization of novel visual information processing systems using photomemristors that mimic human brain functions.