Because wearable biomedical monitoring technologies allow for continuous, real-time, and remote measurement of physiological parameters, they are quickly becoming a key component of contemporary healthcare. These systems place stringent demands on the analog front-end (AFE), which must accurately acquire and condition extremely low-amplitude biopotential signals such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) while operating under severe power and size constraints. This paper presents the design and simulation of an energy-efficient CMOS analog front-end specifically optimized for wearable biomedical monitoring applications. The proposed AFE integrates a low-noise instrumentation amplifier, an active band-limited filter, a programmable gain stage, and a low-power output buffer, all implemented using a standard 180 nm CMOS technology to ensure full on-chip integration and cost effectiveness. Power-aware circuit design techniques, including current scaling, noise-optimized transistor sizing, and low-voltage biasing, are employed to minimize power consumption without compromising signal integrity or stability. The AFE is designed to provide high differential gain, excellent common-mode rejection, and effective suppression of out-of-band noise and interference. Post-layout simulations demonstrate that the proposed design achieves a total power consumption of 210 µW from a 1.8 V supply, a programmable overall gain of up to 60 dB, an input-referred noise density of 6.5 nV/√Hz at 1 kHz, and a common-mode rejection ratio exceeding 90 dB across the signal bandwidth. These performance metrics indicate that the proposed CMOS analog front-end is well suited for low-power, high-fidelity wearable biomedical monitoring systems and represents a practical solution for next-generation portable healthcare devices.

Keywords - CMOS analog front-end, wearable biomedical systems, low power, instrumentation amplifier, ECG, EEG.

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1. Razavi B. Design of integrated circuits for optical communications. John Wiley & Sons; 2012 Aug 21.
2. Gray PR, Hurst PJ, Lewis SH, Meyer RG. Analysis and design of analog integrated circuits. John Wiley & Sons; 2009 Jan 20.
3. Baker RJ. CMOS: circuit design, layout, and simulation. John Wiley & Sons; 2019 Jul 11.
4. Harrison RR, Charles C. A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of solid-state circuits. 2003 Jun 5;38(6):958-65.
5. Wu H, Chen J, Liu X, Zou W, Yang J, Sawan M. An energy-efficient small-area configurable analog front-end interface for diverse biosignals recording. IEEE Transactions on Biomedical Circuits and Systems. 2023 Jul 10;17(4):818-30.
6. Sedra AS, Smith KC, Carusone TC, Gaudet VC. Microelectronic circuits. Oxford: Oxford university press; 2011.
7. Carusone TC, Johns DA, Martin KW. Analog integrated circuit design. John Wiley & Sons; 2011 Dec 13.
8. Bhamra H, Lynch J, Ward M, Irazoqui P. A noise-power-area optimized biosensing front end for wireless body sensor nodes and medical implantable devices. IEEE transactions on very large scale integration (VLSI) systems. 2017 Jun 27;25(10):2917-28.
9. Kim I, Bhagat YA, Homer J, Lobo R. Multimodal analog front end for wearable bio- sensors. IEEE Sensors Journal. 2016 May 9;16(24):8784-91.
10. Baker RJ. CMOS: circuit design, layout, and simulation. John Wiley & Sons; 2019 Jul 11.
11. Yang HY, Sarpeshkar R. A bio-inspired ultra-energy-efficient analog-to-digital converter for biomedical applications. IEEE Transactions on Circuits and Systems I: Regular Papers. 2006 Nov 13;53(11):2349-56.
12. Carusone A, Johns DA. Analogue adaptive filters. Design of High Frequency Integrated Analogue Filters. 2002 Apr 15(14):161.
13. Harrison RR, Charles C. A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of solid-state circuits. 2003 Jun 5;38(6):958-65.
14. Ghafar-Zadeh E, Forouhi S, Azadmousavi T. CMOS-Based Sensors and Actuators for Life Science Applications. The Institution of Engineering and Technology; 2023 Dec 18.
15. Yazicioglu RF, Merken P, Puers R, Van Hoof C. A 60$\mu $ W 60 nV/$\surd $ Hz readout front-end for portable biopotential acquisition systems. IEEE Journal of Solid-State Circuits. 2007 May 31;42(5):1100-10.
16. Wee WK, Jubadi WM. CMOS-based Instrumentation Amplifier for Biomedical Application. Evolution in Electrical and Electronic Engineering. 2021 Nov 14;2(2):129-39.
17. Goel A. A novel low noise high gain CMOS instrumentation amplifier for biomedical applications. International Journal of Electrical and Computer Engineering. 2013 Aug 1;3(4):516.
18. Shivanandham RS, Madhumitha D, Sharmila Parveen M, Roshaan JS, SriSanjana K. A Low-Power High-Gain Two-Stage Cmos Operational Amplifier in 45 Nm Technology for Biomedical Applications With Full Pvt Validation. In2025 IEEE 4th International Conference for Advancement in Technology (ICONAT) 2025 Sep 19 (pp. 1-5). IEEE.
19. Emon MZ, Salim KM, Chowdhury MI. Design and analysis of a high-gain, low-noise, and low-power analog front end for electrocardiogram acquisition in 45 nm technology using gm/ID method. Electronics. 2024 Jun 4;13(11):2190.
20. Khan SR, Nadeem I. Ultra-low power high-input impedance subthreshold CMOS neural front-end amplifier. Radioelectronics and Communications Systems. 2019 Mar;62(3):134-41.
21. Rao DS, Yedukondalu U, Balaji N. A comprehensive survey on low power analog front end circuits with digital converters for biomedical sensor application. Analog Integrated Circuits and Signal Processing. 2026 Jan;126(1):11.
22. Singh W, Deb S, Bahubalindruni P. Low-noise energy efficient readout front-end for wearable continuous blood pressure monitoring systems. IEEE Transactions on Consumer Electronics. 2020 Aug 18;66(4):318-26.
23. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
24. Godase V, Godase J. Diet prediction and feature importance of gut microbiome using machine learning. Evolution in Electrical and Electronic Engineering. 2024 Nov 6;5(2):214-9.
25. Godase V. A comprehensive study of revolutionizing EV charging with solar-powered wireless solutions. Advance Research in Power Electronics and Devices e-ISSN. 2025 Apr 18:3048-7145.
26. Salunkhe A, Pawar V, Pise P, Mule S, Survase A, Godase V, Zambre S. A Review on Real-Time RFID-Based Smart Attendance Systems for Efficient Record Management. Advance Research in Analog and Digital Communications. 2025 Aug;2(2):32-46.
27. Vaibhav VG. A Neuromorphic-Inspired, Low-Power VLSI Architecture for Edge AI in IoT Sensor Nodes. Journal of Microelectronics and Solid State Devices. 2025;12(2):41-7p.

Because wearable biomedical monitoring technologies allow for continuous, real-time, and remote measurement of physiological parameters, they are quickly becoming a key component of contemporary healthcare. These systems place stringent demands on the analog front-end (AFE), which must accurately acquire and condition extremely low-amplitude biopotential signals such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) while operating under severe power and size constraints. This paper presents the design and simulation of an energy-efficient CMOS analog front-end specifically optimized for wearable biomedical monitoring applications. The proposed AFE integrates a low-noise instrumentation amplifier, an active band-limited filter, a programmable gain stage, and a low-power output buffer, all implemented using a standard 180 nm CMOS technology to ensure full on-chip integration and cost effectiveness. Power-aware circuit design techniques, including current scaling, noise-optimized transistor sizing, and low-voltage biasing, are employed to minimize power consumption without compromising signal integrity or stability. The AFE is designed to provide high differential gain, excellent common-mode rejection, and effective suppression of out-of-band noise and interference. Post-layout simulations demonstrate that the proposed design achieves a total power consumption of 210 µW from a 1.8 V supply, a programmable overall gain of up to 60 dB, an input-referred noise density of 6.5 nV/√Hz at 1 kHz, and a common-mode rejection ratio exceeding 90 dB across the signal bandwidth. These performance metrics indicate that the proposed CMOS analog front-end is well suited for low-power, high-fidelity wearable biomedical monitoring systems and represents a practical solution for next-generation portable healthcare devices.

Keywords - CMOS analog front-end, wearable biomedical systems, low power, instrumentation amplifier, ECG, EEG.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Razavi B. Design of integrated circuits for optical communications. John Wiley & Sons; 2012 Aug 21.
2. Gray PR, Hurst PJ, Lewis SH, Meyer RG. Analysis and design of analog integrated circuits. John Wiley & Sons; 2009 Jan 20.
3. Baker RJ. CMOS: circuit design, layout, and simulation. John Wiley & Sons; 2019 Jul 11.
4. Harrison RR, Charles C. A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of solid-state circuits. 2003 Jun 5;38(6):958-65.
5. Wu H, Chen J, Liu X, Zou W, Yang J, Sawan M. An energy-efficient small-area configurable analog front-end interface for diverse biosignals recording. IEEE Transactions on Biomedical Circuits and Systems. 2023 Jul 10;17(4):818-30.
6. Sedra AS, Smith KC, Carusone TC, Gaudet VC. Microelectronic circuits. Oxford: Oxford university press; 2011.
7. Carusone TC, Johns DA, Martin KW. Analog integrated circuit design. John Wiley & Sons; 2011 Dec 13.
8. Bhamra H, Lynch J, Ward M, Irazoqui P. A noise-power-area optimized biosensing front end for wireless body sensor nodes and medical implantable devices. IEEE transactions on very large scale integration (VLSI) systems. 2017 Jun 27;25(10):2917-28.
9. Kim I, Bhagat YA, Homer J, Lobo R. Multimodal analog front end for wearable bio- sensors. IEEE Sensors Journal. 2016 May 9;16(24):8784-91.
10. Baker RJ. CMOS: circuit design, layout, and simulation. John Wiley & Sons; 2019 Jul 11.
11. Yang HY, Sarpeshkar R. A bio-inspired ultra-energy-efficient analog-to-digital converter for biomedical applications. IEEE Transactions on Circuits and Systems I: Regular Papers. 2006 Nov 13;53(11):2349-56.
12. Carusone A, Johns DA. Analogue adaptive filters. Design of High Frequency Integrated Analogue Filters. 2002 Apr 15(14):161.
13. Harrison RR, Charles C. A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of solid-state circuits. 2003 Jun 5;38(6):958-65.
14. Ghafar-Zadeh E, Forouhi S, Azadmousavi T. CMOS-Based Sensors and Actuators for Life Science Applications. The Institution of Engineering and Technology; 2023 Dec 18.
15. Yazicioglu RF, Merken P, Puers R, Van Hoof C. A 60$\mu $ W 60 nV/$\surd $ Hz readout front-end for portable biopotential acquisition systems. IEEE Journal of Solid-State Circuits. 2007 May 31;42(5):1100-10.
16. Wee WK, Jubadi WM. CMOS-based Instrumentation Amplifier for Biomedical Application. Evolution in Electrical and Electronic Engineering. 2021 Nov 14;2(2):129-39.
17. Goel A. A novel low noise high gain CMOS instrumentation amplifier for biomedical applications. International Journal of Electrical and Computer Engineering. 2013 Aug 1;3(4):516.
18. Shivanandham RS, Madhumitha D, Sharmila Parveen M, Roshaan JS, SriSanjana K. A Low-Power High-Gain Two-Stage Cmos Operational Amplifier in 45 Nm Technology for Biomedical Applications With Full Pvt Validation. In2025 IEEE 4th International Conference for Advancement in Technology (ICONAT) 2025 Sep 19 (pp. 1-5). IEEE.
19. Emon MZ, Salim KM, Chowdhury MI. Design and analysis of a high-gain, low-noise, and low-power analog front end for electrocardiogram acquisition in 45 nm technology using gm/ID method. Electronics. 2024 Jun 4;13(11):2190.
20. Khan SR, Nadeem I. Ultra-low power high-input impedance subthreshold CMOS neural front-end amplifier. Radioelectronics and Communications Systems. 2019 Mar;62(3):134-41.
21. Rao DS, Yedukondalu U, Balaji N. A comprehensive survey on low power analog front end circuits with digital converters for biomedical sensor application. Analog Integrated Circuits and Signal Processing. 2026 Jan;126(1):11.
22. Singh W, Deb S, Bahubalindruni P. Low-noise energy efficient readout front-end for wearable continuous blood pressure monitoring systems. IEEE Transactions on Consumer Electronics. 2020 Aug 18;66(4):318-26.
23. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
24. Godase V, Godase J. Diet prediction and feature importance of gut microbiome using machine learning. Evolution in Electrical and Electronic Engineering. 2024 Nov 6;5(2):214-9.
25. Godase V. A comprehensive study of revolutionizing EV charging with solar-powered wireless solutions. Advance Research in Power Electronics and Devices e-ISSN. 2025 Apr 18:3048-7145.
26. Salunkhe A, Pawar V, Pise P, Mule S, Survase A, Godase V, Zambre S. A Review on Real-Time RFID-Based Smart Attendance Systems for Efficient Record Management. Advance Research in Analog and Digital Communications. 2025 Aug;2(2):32-46.
27. Vaibhav VG. A Neuromorphic-Inspired, Low-Power VLSI Architecture for Edge AI in IoT Sensor Nodes. Journal of Microelectronics and Solid State Devices. 2025;12(2):41-7p.