Flexible TiO_(2)-WO_(3−x) hybrid memristorwith enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing  

作　　者：Jianyong Pan Hao Kan Zhaorui Liu Song Gao Enxiu Wu Yang Li Chunwei Zhang 

机构地区：[1]Shandong Provincial Key Laboratory of Network Based Intelligent Computing,School of Information Science and Engineering,University of Jinan,Jinan,China [2]State Key Lab of Transducer Technology,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai,China [3]School of Integrated Circuits,Shandong University,Jinan,China

出　　处：《npj Flexible Electronics》2024年第1期180-191,共12页npj-柔性电子(英文)

基　　金：supported by the National Natural Science Foundation of China under Grant(62104080,62204170);Natural Science Foundation of Shandong Province under Grant(ZR2023ZD03);Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017);Taishan Scholars Project Special Funds(tsqn202312035).

摘　　要：Tungsten oxide(WO_(3))-based memristors show promising applications in neuromorphic computing.However,single-layer WO_(3) memristors suffer from issues such as weak memory performance and nonlinear conductance variations.In this work,a functional layer based on the hybrids of WO_(3−x) and TiO_(2) is proposed for constructing flexible memristors featuring outstanding synaptic characteristics.Applying diverse electrical stimulations to the memristor enables a range of synaptic functions,elucidating its conduction mechanism through the conductive filament model.The incorporation of TiO_(2) not only enhances the memristor’s memory characteristics but makes its conductance more linear,symmetrical and uniform during the long-term changes.Furthermore,in view of the enhanced device performance by TiO_(2) doping,the potential of this device for simple behavioral simulation and processing of complex computing problems is explored.The“learning-forgetting-relearning”characteristics and device integrability are visually demonstrated.Applying the device to a convolutional neural network,the recognition accuracy of MNIST handwritten digits reaches 98.7%.

关 键 词：COMPUTING tuning PRECISE 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]