Relaxation Time of Multipore Nanofluidic Memristors for Neuromorphic Applications

Agustin Bou, Patricio Ramirez, Juan Bisquert
{"title":"Relaxation Time of Multipore Nanofluidic Memristors for Neuromorphic Applications","authors":"Agustin Bou, Patricio Ramirez, Juan Bisquert","doi":"arxiv-2409.09327","DOIUrl":null,"url":null,"abstract":"Memristors have been positioned at the forefront of the purposes for carrying\nout neuromorphic computation. Their tuneable conductivity properties enable the\nimitation of synaptic behaviour. Multipore nanofluidic memristors have shown\ntheir memristic properties and are candidate devices for liquid neuromorphic\nsystems. Such properties are visible through an inductive hysteresis in the\ncurrent-voltage sweeps, which is then confirmed by the inductive\ncharacteristics in impedance spectroscopy measurements. The dynamic behaviour\nof memristors is largely determined by a voltage-dependent relaxation time.\nHere, we obtain the kinetic relaxation time of a multipore nanofluidic\nmemristor via its impedance spectra. We show that the behaviour of this\ncharacteristic of memristors is comparable to that of natural neural systems.\nHence, we open a way to study the mimic of neuron characteristics by searching\nfor memristors with the same kinetic times.","PeriodicalId":501083,"journal":{"name":"arXiv - PHYS - Applied Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Applied Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tuneable conductivity properties enable the imitation of synaptic behaviour. Multipore nanofluidic memristors have shown their memristic properties and are candidate devices for liquid neuromorphic systems. Such properties are visible through an inductive hysteresis in the current-voltage sweeps, which is then confirmed by the inductive characteristics in impedance spectroscopy measurements. The dynamic behaviour of memristors is largely determined by a voltage-dependent relaxation time. Here, we obtain the kinetic relaxation time of a multipore nanofluidic memristor via its impedance spectra. We show that the behaviour of this characteristic of memristors is comparable to that of natural neural systems. Hence, we open a way to study the mimic of neuron characteristics by searching for memristors with the same kinetic times.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于神经形态应用的多孔纳米流体晶膜管的弛豫时间
忆阻器已被定位为神经形态计算的最前沿。它们的可调电导特性能够模拟突触行为。多孔纳米流体忆阻器显示了它们的忆阻特性,是液体神经形态系统的候选器件。这种特性通过电流-电压扫描中的电感滞后显现出来,然后通过阻抗光谱测量中的电感特性得到证实。在这里,我们通过阻抗谱获得了多孔纳米流体忆阻器的动力学弛豫时间。我们的研究表明,忆阻器的这一特性与自然神经系统的特性相当。因此,我们通过寻找具有相同动力学时间的忆阻器,开辟了一条研究模拟神经元特性的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Ultrafast cascade charge transfer in multi bandgap colloidal quantum dot solids enables threshold reduction for optical gain and stimulated emission p-(001)NiO/n-(0001)ZnO Heterostructures based Ultraviolet Photodetectors Normal/inverse Doppler effect of backward volume magnetostatic spin waves Unattended field measurement of bird source level Fabrication of Ultra-Thick Masks for X-ray Phase Contrast Imaging at Higher Energy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1