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Published November 2018 | public
Journal Article

Design and Implementation of Reference-Free Drift-Cancelling CMOS Magnetic Sensors for Biosensing Applications

Abstract

Magnetic imagers, which utilize magnetic nanoparticles as labels to realize biodetection assays, hold significant promise for deployment at the point-of-use. Resonance-shift-based sensors can be realized in standard CMOS processes without post-process modifications and offer great sensitivity at low price tags. Unfortunately, CMOS resonant-shift magnetic sensors suffer significant degradation in SNR and long-term stability due to low on-chip inductor quality factors and significant noise introduced from active devices and thermal variations. This makes standard resonant-shift-based imagers undesirable for use in low-signal biodetection assays. Furthermore, and most importantly, the significant long-term drift due to slow-varying noise sources and temperature changes makes these sensors inadequate for bioexperiments which may take timescales on the order of hours to reach completion. In this paper, we propose a transformer-based approach which enables sub-parts-per-million (PPM) signal detection without the need for any thermal compensation. The approach is self-referencing, leading to significant savings in chip area by removing the need for replica reference cells. We analyze the performance of the transformer-based circuit compared to the original second-order system and demonstrate its superiority for rejecting system noise. A proof-of-concept design of a fully integrated 2×2 CMOS transformer-based magnetic sensor array is presented which achieves reference-free, sub-PPM detection of magnetic signals. The system can be powered and operated completely from a laptop USB interface and each sensing cell can consume less than 3 mW of DC power. Finally, we show the results of an initial DNA biodetection experiment which confirms the capability of the sensor to be used for realistic bioassays.

Additional Information

© 2018 IEEE. Manuscript received May 4, 2018; revised July 12, 2018; accepted July 27, 2018. Date of publication October 11, 2018; date of current version October 22, 2018. This paper was approved by Guest Editor Chung-Yu Wu. The authors would like to thank B. Ling for help with wet-lab experiments, B. Hong for help with layout, and K. Mauser and N. Scianmarello for help with sample preparation.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023