Revolutionizing breast cancer diagnosis: harnessing heterojunction vertical tunnel FET with magnesium silicide
Abstract This study investigates the sensitivity characteristics of breast cancer biomarkers associated with both tumorigenic (MCF-7, T47D, Hs578T, and MDA-MB-231) and non-tumorigenic (MCF-10A) cell lines, utilizing the high-sensitivity regime of a Magnesium Silicide (Mg 2 Si)-based Vertical Tunnel Field-Effect Transistor (VTFET) sensor. The biosensing performance of the proposed Mg 2 Si-VTFET sensor is systematically evaluated through a comprehensive analysis of its electrical behavior, including surface potential, energy band diagrams, band-to-band tunneling (BTBT) rate, drive current, and transconductance. Key sensitivity parameters such as ON-state current (I ON ), I ON /I OFF ratio, transconductance, and subthreshold swing (SS) are employed as quantitative sensing metrics. Additionally, the study addresses the influence of steric hindrance during biomarker immobilization on the sensing performance. Device optimization is carried out by modulating the cavity thickness to enhance detection efficiency. The proposed Mg 2 Si-VTFET sensor exhibits a peak sensitivity of 2.2 × 10 6 , with the highest selectivity observed for the T47D breast cancer cell line. These findings demonstrate a significant enhancement in sensitivity and selectivity, and owing to its CMOS compatibility, the Mg 2 Si-VTFET sensor emerges as a promising candidate for label-free, selective detection of both cancerous and non-cancerous breast biomarkers. However, the Mg 2 Si-VTFET sensor can also be employed to sense other biomolecules based on dielectric modulation approach.
Authors
- Varun Mishra (ORCID: https://orcid.org/0000-0002-9264-9338)
- Pankaj Kumar (ORCID: https://orcid.org/0000-0001-7172-5541)
- Akhilesh Chauhan
- Rahul Nautiyal
Institutions
- ITM University (IN)
- Indian Institute of Technology Dharwad (IN)
- Graphic Era University (IN)
Publication Details
- Journal
- Physica Scripta
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1088/1402-4896/ae06d3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00