A Low Noise CMOS Sensor Frontend for a TMR-based Biosensing Platform

Mohamed, A., Schmid, M., Tanwear, A., Heidari, H. and Anders, J. (2020) A Low Noise CMOS Sensor Frontend for a TMR-based Biosensing Platform. In: IEEE Sensors 2020, 25-28 Oct 2020, ISBN 9781728168012 (doi: 10.1109/SENSORS47125.2020.9278826)

Abstract

In this paper, we propose a low noise CMOS frontend for a Point-of-Care (PoC) biosensing platform based on tunnel magnetoresistance (TMR) as sensors. The integration of a low noise and low power integrated circuit (IC) with the TMR sensors reduces power consumption compared to a realization with discrete electronics, and thereby paves the way towards a portable diagnostic system. The proposed chip uses a DC-coupled fully differential difference amplifier (FDDA) to amplify the minute signals generated by magnetic nanotags (MNTs) that will be used as biomarkers in the target biosensing application. The FDDA features a gain of around 60dB with a suitable offset calibration scheme to deal with the large DC offsets caused by TMR and/or magnetic field variations. The ability to deal with changing DC fields is crucial for a portable setup that is intended to be used in unshielded environments outside the lab. The offset cancellation is achieved by two on-chip current steering DACs that can accommodate TMR resistances between 535Ω and 4.7kΩ. The presented chip is manufactured in a 180nm SOI CMOS technology and features a thermal noise floor of 7nV/√Hz. It consumes a total of 7.7mA from a 1.8V supply.

• Item Type: Conference Proceedings
• Additional Information: This work has been supported by the German Research Foundation (DFG) under contract no. AN 984/12-1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tanwear, Dr Asfand and Heidari, Professor Hadi
• Authors: Mohamed, A., Schmid, M., Tanwear, A., Heidari, H., and Anders, J.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISSN: 2168-9229
• ISBN: 9781728168012
• Published Online: 09 December 2020
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in IEEE Sensors 2020
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Related URLs

• Organisation