Low power CMOS Gm-C based low pass filter for front end neural signal processing

Ashish Dixit, G. Srivastava, Anil Kumar, S. Shukla
{"title":"Low power CMOS Gm-C based low pass filter for front end neural signal processing","authors":"Ashish Dixit, G. Srivastava, Anil Kumar, S. Shukla","doi":"10.11591/ijpeds.v15.i1.pp559-565","DOIUrl":null,"url":null,"abstract":"The sub 100 µV voltage levels and sub 100 Hz frequency range makes the processing of most popular signal electroencephalograph (EEG) for brain functionality analysis, a complex task. The low frequency content of EEG (useful signals below 70 Hz) is commonly used for diagnosis of various brain related disorders making low-pass filter (LPF) a key block in front-end processing as noise reduction and resolution enhancement is crucial for precise recovery of these information. This paper is aimed to design reduced transconductance (Gm) based low power and small area CMOS LPF with cutoff frequency (fc) around 70 Hz. The proposed design is simulated using Cadence virtuoso tool and gives cut-off frequency of 72.958 Hz with low output noise of 3.0609 µV/√Hz and power consumption of 264.060 nW at operating voltage of 0.4 V. The simulation results show linearity of performance over -40 to 100 °C. Layout of circuit takes up area of 86.74×81.21 µm and post layout simulation shows 5% variation in power consumption as compared to pre layout simulations.","PeriodicalId":355274,"journal":{"name":"International Journal of Power Electronics and Drive Systems (IJPEDS)","volume":"115 28","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Power Electronics and Drive Systems (IJPEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijpeds.v15.i1.pp559-565","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

The sub 100 µV voltage levels and sub 100 Hz frequency range makes the processing of most popular signal electroencephalograph (EEG) for brain functionality analysis, a complex task. The low frequency content of EEG (useful signals below 70 Hz) is commonly used for diagnosis of various brain related disorders making low-pass filter (LPF) a key block in front-end processing as noise reduction and resolution enhancement is crucial for precise recovery of these information. This paper is aimed to design reduced transconductance (Gm) based low power and small area CMOS LPF with cutoff frequency (fc) around 70 Hz. The proposed design is simulated using Cadence virtuoso tool and gives cut-off frequency of 72.958 Hz with low output noise of 3.0609 µV/√Hz and power consumption of 264.060 nW at operating voltage of 0.4 V. The simulation results show linearity of performance over -40 to 100 °C. Layout of circuit takes up area of 86.74×81.21 µm and post layout simulation shows 5% variation in power consumption as compared to pre layout simulations.
基于低功耗 CMOS Gm-C 的前端神经信号处理低通滤波器
低于 100 µV 的电压水平和低于 100 Hz 的频率范围使得处理用于大脑功能分析的最常用脑电图(EEG)信号成为一项复杂的任务。脑电图的低频内容(低于 70 Hz 的有用信号)通常用于诊断各种与大脑有关的疾病,因此低通滤波器 (LPF) 成为前端处理的关键模块,因为降噪和提高分辨率对于精确恢复这些信息至关重要。本文旨在设计基于降低跨导(Gm)的低功耗、小面积 CMOS LPF,其截止频率(fc)约为 70 Hz。利用 Cadence virtuoso 工具对所提出的设计进行了仿真,结果表明,在工作电压为 0.4 V 时,截止频率为 72.958 Hz,输出噪声为 3.0609 µV/√Hz,功耗为 264.060 nW。仿真结果表明,在 -40 至 100 °C 温度范围内具有线性性能。电路布局占地面积为 86.74×81.21 µm,布局后仿真显示功耗与布局前仿真相比有 5%的变化。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信