A Comprehensive Analytical Approach to Introspect Efficient Miniaturized Circuit-Level Designs for Biomedical Signal Acquisition: A Tutorial Brief

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Authors: Umar Mohammad, Ying Zhang, Muhammad Zada, Maolin Zhang, Fang Tang, Yufeng Guo, Amine Bermak
Cited by: 2
References: 47

Abstract

Health care technology is advancing rapidly, transforming diagnostics through compact, non-invasive, and user-friendly biomedical devices. For instance, diabetes diagnosis that once required significant blood volume can now be achieved in seconds using a nano-pinch sample. Recent developments in portable and wearable systems have minimized the need for invasive procedures, enabling continuous, real-time monitoring, especially among aging populations. This growing demand underscores the need for compact, energy-efficient, and affordable biomedical systems seamlessly integrated into daily life. This tutorial brief provides an in-depth analytical perspective on low-noise, low-cost, and low-power circuit-level design strategies for next-generation biomedical devices. Challenges and escape-out methods for low-noise design implementations are broadly discussed in this work. The study consolidates noise-reduction techniques, such as correlated double sampling and multi-stage amplifier configurations, and highlights their impact on signal integrity, power efficiency, and scalability. It also addresses design trade-offs and cost considerations, offering a practical framework for researchers developing efficient analog front-end architectures for wearable and implantable biomedical applications. Finally, the results presented in this work were carried out in Cadence Virtuoso using 65nm CMOS TSMC technology node.

Fields of Study & Subjects

Analog and Mixed-Signal Circuit Design Low-power high-performance VLSI design Neuroscience and Neural Engineering

Publication details

Journal / source
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
26
Issue
5
Pages
6567–6578
Publication date
12 January 2026