@inproceedings{1d25bb4c90cb4150a7017e3b31275217,
title = "An ultra low power, configurable intelligent biasing calibration for medical sensor applications in 130 nm cmos technology",
abstract = "In this paper, a configurable intelligent biasing calibration methodology is presented for medical sensor applications. The proposed biasing calibration algorithm is applicable for single as well as an array of nanopore sensors in a medical device. This technique compensates the variation among different nanopore sensors by calibrating the gate biasing voltages and improves accuracy and enhances reliability. It also identifies the faulty nanopore sensors. The presented biasing calibration controller is fully synthesizable and it needs only 1.184 K gates for its implementation. It draws only 76.3 nA current from 1.2 V supply its power consumption is only 91.56 nW. The proposed intelligent biasing calibration controller is integrated into a medical sensing system and it is implemented in a 130 nm CMOS process.",
keywords = "Analog-front-end, Calibration, CMOS, Digital-controller, Nanopore",
author = "Imran Ali and Huo Yingge and Muhammad Asif and Lee, \{Kang Yoon\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 ; Conference date: 19-02-2020 Through 22-02-2020",
year = "2020",
month = feb,
doi = "10.1109/BigComp48618.2020.00017",
language = "English",
series = "Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "593--596",
editor = "Wookey Lee and Luonan Chen and Yang-Sae Moon and Julien Bourgeois and Mehdi Bennis and Yu-Feng Li and Young-Guk Ha and Hyuk-Yoon Kwon and Alfredo Cuzzocrea",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020",
}