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  4. Sub-9×29 μm2, 1.2nW, Fully Digital Potentiostat Design for Dopamine Sensing
 
research article

Sub-9×29 μm2, 1.2nW, Fully Digital Potentiostat Design for Dopamine Sensing

Meimandi, Ali  
•
Barbruni, Gian Luca  
•
Crovetti, Paolo Stefano
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2025
IEEE Sensors Journal

This paper presents a highly miniaturized and low-power CMOS electrochemical potentiostat based on a fully digital design. The potentiostat functionality has been evaluated through dopamine detection using proper microelectrodes. The system drives an electrochemical cell in a three-electrode configuration and implements the chronoamperometric technique to minimize both power consumption and required area. The circuit has been fabricated using TSMC 180 nm CMOS technology. Supplied at 0.4 V, the potentiostat alone consumes the up-to-date smallest power of 1.2 nW and occupies the up-to-date smallest ever area of 260 μm2. Together with its voltage reference and oscillator, the entire system consumes 9.2 nW and occupies 370 μm2. The potentiostatic voltage maintains an accuracy of ±10% (180 mV to 220 mV) for a 200 mV reference and supply voltage ranging from 0.4 V to 0.56 V, at a frequency of 25 kHz, driven by the ring oscillator. In-vitro experiments demonstrate dopamine detection with a sensitivity of 18.74 pulses/μM and a limit of detection of 0.525 μM. Results demonstrate the huge advantage of using the proposed fully-digital design, making this potentiostat well-suited for implantable devices where size and energy efficiency are critical (e.g., Neural Dust).

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Type
research article
DOI
10.1109/JSEN.2025.3605728
Scopus ID

2-s2.0-105016322722

Author(s)
Meimandi, Ali  

École Polytechnique Fédérale de Lausanne

Barbruni, Gian Luca  

École Polytechnique Fédérale de Lausanne

Crovetti, Paolo Stefano

Politecnico di Torino

Carrara, Sandro  

École Polytechnique Fédérale de Lausanne

Date Issued

2025

Published in
IEEE Sensors Journal
Subjects

Biomedical Implants

•

Body Dust

•

CMOS design

•

Digital OTA

•

Digital Potentiostat

•

Electrochemical Sensing

•

Neural Dust

•

Ultra-Miniaturized and Ultra-Low Power IC

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

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Available on Infoscience
September 29, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/254414
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