Electropolymerized dopamine-based memristors using threshold switching behaviors for artificial current-activated spiking neurons  

作　　者：Bowen Zhong Xiaokun Qin Zhexin Li Yiqiang Zheng Lingchen Liu Zheng Lou Lili Wang 

机构地区：[1]State Key Laboratory for Superlattices and Microstructures,Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China [2]Center of Materials Science and Optoelectronic Engineering,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Journal of Semiconductors》2025年第2期98-103,共6页半导体学报(英文版)

基　　金：support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009);National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159);from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).

摘　　要：Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

关 键 词：ELECTROPOLYMERIZATION POLYDOPAMINE MEMRISTOR threshold switching spiking voltage artificial neuron 

分 类 号：TN60[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]