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

作　　者：Bo Chen Baojie Zhu Yifan Wu Pengpeng Sang Jixuan Wu Xuepeng Zhan Jiezhi Chen 

机构地区：[1]School of Information Science and Engineering(ISE),Shandong University,Qingdao 266237,China

出　　处：《Journal of Semiconductors》2024年第12期130-134,共5页半导体学报（英文版）

基　　金：supported by National Key Research and Development Program of China (2023YFB4402500,2023YFB4402400);National Natural Science Foundation of China (621041314);Natural Science Foundation of Shandong Province (ZR2023LZH007);Program of Qilu Young Scholars of Shandong University.

摘　　要：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 mem-ory characteristics,distinct conductive states are achieved,which are further utilized to construct RC networks.With a third con-trol 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 memris-tors via laser processing.

关 键 词：chaotic graphene oxide laser processing reservoir computing 

分 类 号：TP333[自动化与计算机技术—计算机系统结构] TQ127.11[自动化与计算机技术—计算机科学与技术] TB383.2[化学工程—无机化工]