文献类型：期刊文献

英文题名：Polymer memristor for information storage and neuromorphic applications

作者：Chen, Yu[1,3];Liu, Gang[2];Wang, Cheng[1];Zhang, Wenbin[2];Li, Run-Wei[2];Wang, Luxing[1]

机构：[1]E China Univ Sci & Technol, Inst Appl Chem, Key Lab Adv Mat, Shanghai 200237, Peoples R China;[2]Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Key Lab Magnet Mat & Devices, Ningbo 315201, Zhejiang, Peoples R China;[3]Fudan Univ, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China

年份：2014

卷号：1

期号：5

起止页码：489

外文期刊名：MATERIALS HORIZONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000348204900002)】；

基金：The authors are grateful for the financial support of the National Natural Science Foundation of China (51333002, 21074034, 51303194, 61328402), Research Fund for the Doctoral Program of Higher Education of China (20120074110004), the State Key Project of Fundamental Research of China (973 Program, 2012CB933004), the Chinese Academy of Sciences, the Fundamental Research Funds for the Central Universities, the State Key Laboratory of ASIC & System of Fudan University (11KF007), Ningbo Science and Technology Innovation Team (2011B82004), Ningbo Natural Science Foundation (2013A610031) and the Shanghai Leading Talents program.

语种：英文

摘要：Polymermaterials have been considered as promising candidates for the implementation of memristor devices due to their low-cost, easy solution processability, mechanical flexibility and ductibility, tunable electronic performance through innovative molecular design cum synthesis strategy and compatibility with complementary metal oxide semiconductor (CMOS) technology as well. The digital-type polymer memristor behaves as resistive random access memory with non-volatility, high density, more speed, low power consumption, large ON/OFF ratio, high endurance and long retention, and is recognized as an appealing candidate for the next generation "universal memory". As a logic component, the analog-type memristor, with the ability to emulate the fundamental synaptic functions of short-term/long-term plasticity (STP/LTP), spike-timing dependent-plasticity (STDP), spike-rate dependent plasticity (SRDP) and "learningexperience" behaviors, can be used to construct artificial neural networks for neuromorphic computation. In this review, we shall attempt to summarize the recent progress in research on the materials, switching characteristics and mechanism aspects of two terminal polymer memristors, for both information storage and neuromorphic applications that inspire great interest in the industrial and academic communities.