Demonstration of CMOS-compatible memristor-based electrochemical biosensor transducer with threshold-sensing functionality
Many electrochemical biosensors operate based on a threshold-sensing (TS) method, which indicates the presence of a disease when the concentration of a biomarker exceeds a predetermined, disease-specific threshold. The TS in biosensor systems is often implemented using power-hungry signal processing (SP) modules or computers, which increases energy consumption and system complexity. Here, we propose a memristor-based bio-to-electrical transducer with built-in TS functionality, allowing TS to be performed directly within the transducer instead of relying on SP modules and computers. Fabricated resistive random-access memory-based TaO<sub>X</sub>/Ta<sub>2</sub>O<sub>5</sub> memristors meet the transducer requirements, such as a high on/off ratio greater than 30 while maintaining a long unit pulse width exceeding 10 μs. The intended operation of the proposed transducer was experimentally confirmed by the immediate change in resistance of the memristor (R<sub>M</sub>) from high resistance state to low resistance state. Using this proposed transducer, a complete electrochemical biosensor system was implemented by integrating a sensor electrode for pH sensing, an SP module, and a display with the proposed transducer. The memristor-based system offers flexible control of the threshold pH point through a simple design, making it well-suited for point-of-care diagnostics, where portability is highly essential.
Authors
- Jongwon Lee (ORCID: https://orcid.org/0000-0002-2114-8982)
- Choul‐Young Kim (ORCID: https://orcid.org/0000-0002-5532-7399)
- K. Theyagarajan (ORCID: https://orcid.org/0000-0002-2959-5648)
- Aryeong Lee
- Sairaman Saikrithika (ORCID: https://orcid.org/0000-0002-5459-5332)
- Gapseop Sim
- See‐On Park (ORCID: https://orcid.org/0000-0002-4273-4438)
- Hyoungho Ko (ORCID: https://orcid.org/0000-0001-5348-3585)
- Seok Jae Lee (ORCID: https://orcid.org/0000-0003-1523-0012)
- Jae‐Hyuk Ahn (ORCID: https://orcid.org/0000-0001-7490-000X)
- H.-K. Cho (ORCID: https://orcid.org/0009-0000-2186-0817)
- Nam Ho Bae (ORCID: https://orcid.org/0000-0003-2561-7739)
- Yumin Park (ORCID: https://orcid.org/0000-0002-8483-3290)
- Kyoung G. Lee (ORCID: https://orcid.org/0000-0003-2691-0910)
- Shinhyun Choi (ORCID: https://orcid.org/0000-0002-5852-3360)
- Kyung Min Kim (ORCID: https://orcid.org/0000-0002-0190-4226)
- Won-Chul Lee (ORCID: https://orcid.org/0000-0001-8253-0010)
- Young‐Joon Kim (ORCID: https://orcid.org/0000-0003-3511-3438)
- Min‐Ho Kang (ORCID: https://orcid.org/0000-0002-1342-0077)
- Youngjoo Kim (ORCID: https://orcid.org/0000-0002-9677-163X)
- Younghyun Lee (ORCID: https://orcid.org/0000-0002-8169-8978)
- Youna Kwon
- Woo‐Suk Sul
- Youngwoo Yoo (ORCID: https://orcid.org/0009-0002-3084-2631)
Institutions
- Gachon University (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Chungnam National University (KR)
- National NanoFab Center (KR)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1038/s41467-025-66372-w
- Citations
- 4
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.92
Funders
- National Research Foundation
- Ministry of Trade, Industry and Energy
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Korea Evaluation Institute of Industrial Technology
- National NanoFab Center