Advanced low-power filter architecture for biomedical signals with adaptive tuning.

This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region. The major contribution in this work are the filter is made to operate in the weak inversion zone to reduce the area needed for the capacitor and the amount of power dissipated. Process variation is one of the major issues in the weak inversion regime. To overcome this, a unique method of compensating against fluctuations in process, voltage, and temperature is put forth based on magnitude comparison is another contribution. Key findings from post-layout simulations and experimental measurements demonstrate that the filter achieves a tunable cutoff frequency range of 0.5 Hz to 150 Hz, with a total power dissipation of only 6nW at 150 Hz. The design occupies a compact silicon area of 0.065 mm2 and offers a dynamic range of 75 dB. The measured results indicate that for a 300 mVpp signal swing, the top bound on THD is -40 dB. The filter's robustness against process, voltage, and temperature variations is validated through on-chip tuning using a current steering DAC, ensuring stable performance across different operating conditions. These results make the proposed filter a promising candidate for low-power biomedical devices. The recommended filter is developed and implemented using UMC-0.18μm CMOS technology with a 1.0V supply, and the IC is tapped out using an MPW run of Euro practice IC services.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2025 Ramasamy Srinivasagan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)