Laser processing induced nonvolatile memory in chaotic graphene oxide films for flexible reservoir computing applications

Abstract Graphene oxide, as a 2D material with nanometer thickness, offers ultra-high mobility, chaotic properties, and low cost. These make graphene oxide memristors beneficial for reservoir computing (RC) networks. In this study, continuous-wave (CW) laser processing is used to reduce chaotic graphene oxide (CGO) films, resulting in the non-volatile storage capability based on the reduced chaotic graphene oxide (rCGO) films. Laser power significantly impacts the characteristics of the rCGO memristor. Material characterization indicates that laser radiation can effectively reduce the oxygen content in CGO films. With optimized laser power, the rCGO memristor achieves a large ratio at 18 mW laser power. Benefiting from the short-term memory characteristics, distinct conductive states are achieved, which are further utilized to construct RC networks. With a third control probe, the rCGO memristor can express rich reservoir states, demonstrating accuracy in predicting the Hénon map with an NRMSE below 0.3. These findings provide the potential for developing flexible RC networks based on graphene oxide memristors via laser processing.