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Machine-Learning-Assisted Buried-Window FET Sensors for High-Reliability and High-Sensitivity Applications

Mehrad, Mahsa and Zareiee, Meysam (2026) Machine-Learning-Assisted Buried-Window FET Sensors for High-Reliability and High-Sensitivity Applications. Sensors, 26 (4). p. 1171. ISSN 1424-8220

Item Type: Article

Abstract

This paper presents a novel Double Buried-Window Junctionless Field-Effect Transistor (DBW-FET) designed for high-sensitivity, label-free biosensing applications. The proposed device integrates two buried windows, one N-type and one P-type, beneath the active channel within the buried oxide layer, along with two nanocavities serving as biomolecular recognition sites. The dual buried windows form two depletion regions that enhance electrostatic coupling, suppress short-channel effects, and improve biomolecular sensitivity. Numerical simulations using Silvaco TCAD Atlas were performed to investigate device performance under various biomolecular binding conditions. Results show that the DBW-FET exhibits higher drain current, lower subthreshold swing, and improved sensitivity compared with a conventional junctionless FET (C-FET). Furthermore, a machine-learning-assisted optimization framework employing Gaussian Process Regression (GPR) and Bayesian Optimization (BO) was implemented to identify optimal buried window parameters. The optimized design achieved a 20–25% improvement in current sensitivity while maintaining low leakage. These findings demonstrate that the proposed DBW-FET offers a promising and Complementary Metal-Oxide-Semiconductor (CMOS)-compatible architecture for next-generation nanoscale biosensors.

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Identifiers

Item ID: 19974
Identification Number: 10.3390/s26041171
ISSN: 1424-8220
URI: https://sure.sunderland.ac.uk/id/eprint/19974
Official URL: https://www.mdpi.com/1424-8220/26/4/1171

Users with ORCIDS

ORCID for Mahsa Mehrad: ORCID iD orcid.org/0009-0003-6640-5983
ORCID for Meysam Zareiee: ORCID iD orcid.org/0000-0002-5637-1746

Catalogue record

Date Deposited: 24 Feb 2026 11:15
Last Modified: 24 Feb 2026 11:15

Contributors

Author: Mahsa Mehrad ORCID iD
Author: Meysam Zareiee ORCID iD

University Divisions

Faculty of Business and Technology > School of Computer Science and Engineering

Subjects

Engineering

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