A multi-terminal ion-controlled transistor with multifunctionality and wide temporal dynamics for reservoir computing  

作　　者：Kekang Liu Jie Li Fangzhou Li Yiyuan Lin Hongrui Liu Linzi Liang Zhiyuan Luo Wei Liu Mengye Wang Feichi Zhou Yanghui Liu 

机构地区：[1]School of Materials,Sun Yat-Sen University,Shenzhen 518107,China [2]School of Microelectronics,Southern University of Science and Technology,Shenzhen 518055,China [3]School of Software,East China Jiaotong University,Nanchang 330013,China

出　　处：《Nano Research》2024年第5期4444-4453,共10页纳米研究（英文版）

基　　金：supported by Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011272);the National Natural Science Foundation of China(Nos.61904208,62104091,52273246);Guangdong Natural Science Foundation(No.2022A1515011064);Young Innovative Talent Project Research Program(No.2021KQNCX077);Shenzhen Science and Technology Program(Nos.JCYJ20190807155411277,JCYJ20220530115204009).

摘　　要：Reservoir computing(RC)is an energy-efficient computational framework with low training cost and high efficiency in processing spatiotemporal information.The state-of-the-art fully memristor-based hardware RC system suffers from bottlenecks in the computation efficiencies and accuracy due to the limited temporal tunability in the volatile memristor for the reservoir layer and the nonlinearity in the nonvolatile memristor for the readout layer.Additionally,integrating different types of memristors brings fabrication and integration complexities.To overcome the challenges,a multifunctional multi-terminal electrolyte-gated transistor(MTEGT)that combines both electrostatic and electrochemical doping mechanisms is proposed in this work,integrating both widely tunable volatile dynamics with high temporal tunable range of 10^(2) and nonvolatile memory properties with high long-term potentiation/long-term depression(LTP/LTD)linearity into a single device.An ion-controlled physical RC system fully implemented with only one type of MTEGT is constructed for image recognition using the volatile dynamics for the reservoir and nonvolatility for the readout layer.Moreover,an ultralow normalized mean square error of 0.002 is achieved in a time series prediction task.It is believed that the MTEGT would underlie next-generation neuromorphic computing systems with low hardware costs and high computational performance.

关 键 词：reservoir computing multi-terminal electrolyte-gated transistor ionic controlling rich dynamics nonlinear dynamical prediction 

分 类 号：TN32[电子电信—物理电子学]