摘要
电解质栅控晶体管(Electrolyte-gated transistors,EGTs)的沟道电导连续可调特性使其在构建神经形态计算系统中具有巨大应用潜力。本工作以非晶态Nb_(2)O_(5)作为沟道材料,Li_(x)SiO_(2)作为栅电解质材料,制备了一种具备低沟道电导(~120 nS)的EGT器件。该器件利用Li+嵌入/脱出Nb_(2)O_(5)晶格导致的沟道电导连续可逆变化,模拟了神经突触的短程可塑性(Short-term plasticity,STP)、长程可塑性(Long-term plasticity,LTP)以及STP向LTP的转变等功能。基于这种EGT突触特性,本工作设计了关联学习电路,实现了突触权重的负反馈调节,并模拟了“巴普洛夫的狗”经典条件反射行为。这些结果展现出EGT作为神经突触器件的巨大潜力,为实现神经形态计算硬件提供了器件参考。
The analog channel conductance modulation of electrolyte-gated transistors(EGTs)is a desirable property for the emulation of synaptic weight modulation and thus gives them great potential in neuromorphic computing systems.In this work,an all-solid-state electrochemical EGT was introduced with a low channel conductance(~120 nS)using amorphous Nb_(2)O_(5) and Li-doped SiO_(2)(Li_(x)SiO_(2))as the channel and gate electrolyte materials,respectively.By adjusting the applied gate voltage pulse parameters,the reversable and nonvolatile modulation of channel conductance were achieved,which was ascribed to reversible intercalation/deintercalation of Li+ions into/from the Nb_(2)O_(5) lattice.Essential functionalities of synapses,such as the short-term plasticity(STP),long-term plasticity(LTP),and transformation from STP to LTP,were simulated successfully by conductive channel modulation of the EGTs.Based on these characteristics,a simple associative learning circuit was designed by parallel a resistor between the gate and the source terminals.The Pavlovian dog classical conditioning behavior was simulated based on associative learning circuit,where the resistor represented the unconditioned synapse and shared the gate voltage with EGT according to the proportion of its resistance,and the resistance between gate and source for negative feedback regulation of synaptic weights.These results demonstrate the potential of EGT for artificial synaptic devices and provide an insight into hardware implementation of neuromorphic computing systems.
作者
方仁瑞
任宽
郭泽钰
徐晗
张握瑜
王菲
张培文
李悦
尚大山
FANG Renrui;REN Kuan;GUO Zeyu;XU Han;ZHANG Woyu;WANG Fei;ZHANG Peiwen;LI Yue;SHANG Dashan(Key Laboratory of Microelectronic Devices and Integrated Technology,Institute of Microelectronics,Chinese Academy of Sciences,Beijing 100029,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《无机材料学报》
SCIE
EI
CAS
CSCD
北大核心
2023年第4期399-405,共7页
Journal of Inorganic Materials
基金
国家重点基础研究发展计划(2018YFA0701500)
国家自然科学基金(61874138)。
关键词
电解质栅控晶体管
神经突触
突触可塑性
关联学习
electrolyte-gated transistor
synapse
synaptic plasticity
associative learning
作者简介
方仁瑞(1994-),男,博士研究生.E-mail:fangrenrui@ime.ac.cn;通信作者:尚大山,研究员.E-mail:shangdashan@ime.ac.cn。
