Leaky integrate-and-fire and oscillation neurons based on ZnO diffusive memristors for spiking neural networks  

基于ZnO扩散型忆阻器的漏电整合发放神经元和振荡神经元用于脉冲神经网络

作　　者：Liang Wang Le Zhang Shuaibin Hua Qiuyun Fu Xin Guo 汪亮;张乐;化帅斌;傅邱云;郭新

机构地区：[1]Engineering Research Center for Functional Ceramics of Ministry of Education,School of Integrated Circuits,Huazhong University of Science and Technology,Wuhan 430074,China [2]State Key Laboratory of Material Processing and Die&Mould Technology,School of Materials Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Science China Materials》2025年第4期1212-1219,共8页中国科学(材料科学)(英文版)

基　　金：supported by the National Natural Science Foundation of China(62471190);the Natural Science Foundation of Hubei Province,China(2022CFA031)。

摘　　要：Diffusive threshold switching(TS)memristors have emerged as a promising candidate for artificial neurons,effectively replicating neuronal functions and enabling spiking neural networks(SNNs)to emulate the low-power processing of biological brains.In this study,we present an artificial neuron based on a Pt/Ag/ZnO/Pt volatile memristor,which exhibits exceptional TS characteristics,including electro-forming-free operation,low voltage requirements(<0.2 V),high stability(2.25%variation over 1024 cycles),a high on/off ratio(106),and inherent self-compliance.These Pt/Ag/ZnO/Pt diffusive memristors are employed to simultaneously emulate oscillation neurons and leaky integrate-and-fire(LIF)neurons,enabling precise modulation of oscillation and firing frequencies through pulse parameters while maintaining low energy consumption(1.442 nJ per spike).We further integrate the oscillation and LIF neurons as input and output neurons,respectively,in a two-layer SNN,achieving a high classification accuracy of 89.17%on MNIST-based voltage images.This work underscores the potential of ZnO diffusive memristors in emulating stable artificial neurons and highlights their promise for advanced neuromorphic computing applications using SNNs.扩散型阈值转变忆阻器已成为人工神经元的有前景候选者,能够有效模拟神经元功能,并通过脉冲神经网络模拟生物大脑的低功耗处理.在本研究中,我们提出了一种基于Pt/Ag/ZnO/Pt扩散型忆阻器的人工神经元,该忆阻器展现出优异的阈值开关特性,包括不需要电预处理、低电压工作(<0.2 V)、高稳定性(在1024个循环中的变化系数仅为2.25%)、高开关比(10~6)以及固有的自适应特性.这些Pt/Ag/ZnO/Pt扩散型忆阻器被用于同时模拟振荡神经元和泄漏积分发放(LIF)神经元,通过改变脉冲参数精确调节振荡和发放频率,同时保持低能耗(每个脉冲1.442 nJ).我们进一步将振荡神经元和LIF神经元分别集成作为输入和输出神经元,构建了一个两层的脉冲神经网络,在基于MNIST的电压图像上实现了89.17%的高分类准确率.这项工作强调了ZnO扩散型忆阻器在模拟人工神经元方面的潜力,并突显了它们在使用脉冲神经网络进行先进类脑计算应用中的前景.

关 键 词：threshold-switching memristor volatile diffusive memristor oscillation neurons leaky integrate-and-fire neurons spiking neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]