Suppr超能文献

一种用于多通道生理信号采集的低失调模拟前端集成电路。

A low-offset analogue front-end IC for multi-channel physiological signal acquisition.

作者信息

Zhang Jinyong, Wang Lei, Yu Li, Yang Yabei, Zhang Yuanting, Li Bin

机构信息

Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4473-6. doi: 10.1109/IEMBS.2009.5333824.

Abstract

This paper describes a low-offset analogue front-end (AFE) integrated circuit (IC) for multi-channel physiological signal acquisitions. The mixed signal IC consists of low-offset gain programmable instrumentation amplifiers (GPIAs), high sensitive current-to-voltage converters (I-V converters), reference and an 8-bit analogue-to-digital converter (ADC). The IC offered adjustable gains that were elaborated for various physiological signal acquisitions. The conditional signals were quantized by ADC that offered an optimal solution for portable applications. The circuit was fabricated in SMIC 0.18-mum mixed-signal CMOS technology, and core area of the whole IC measured 1.36 mm(2). The post-annotated simulations suggested that the system achieved a common-mode-rejection-ration (CMRR) of 142 dB, the adjustable gain from 31.6 dB to 76.5 dB, and the offset voltage less than 80 microV. The 8-bit ADC exhibited less than 0.8 LSB DNL and 1.1 LSB INL. Power dissipation of each channel and the ADC were approximately 348 microW and 1.65 mW under a 1.8 V single supply voltage, respectively. It is suitable for a wide range of high precision biomedical applications.

摘要

本文描述了一种用于多通道生理信号采集的低失调模拟前端(AFE)集成电路(IC)。该混合信号IC由低失调增益可编程仪表放大器(GPIA)、高灵敏度电流-电压转换器(I-V转换器)、基准和一个8位模数转换器(ADC)组成。该IC提供了可调节增益,针对各种生理信号采集进行了优化。条件信号由ADC进行量化,为便携式应用提供了最佳解决方案。该电路采用中芯国际0.18μm混合信号CMOS工艺制造,整个IC的核心面积为1.36平方毫米。带注释的后仿真表明,该系统实现了142dB的共模抑制比(CMRR)、31.6dB至76.5dB的可调节增益以及小于80μV的失调电压。8位ADC的差分非线性(DNL)小于0.8LSB,积分非线性(INL)小于1.1LSB。在1.8V单电源电压下,每个通道和ADC的功耗分别约为348μW和1.65mW。它适用于广泛的高精度生物医学应用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验