Title: Development of an intelligent mathematical model for n-channel FinFET using improved MobileNet architecture

Authors: Vijayalaxmi Kumbar; Manisha G. Waje

Addresses: Department of Electronics and Telecommunication Engineering, G H Raisoni College of Engineering and Management, affiliated to Savitribai Phule Pune University, Pune, Maharashtra, 412207, India; Department of Electronics and Telecommunication Engineering, PCET's PCCOE&R, Ravet, Pune, Maharashtra, 412101, India ' Department of Electronics and Telecommunication Engineering, G H Raisoni College of Engineering and Management, affiliated to Savitribai Phule Pune University, Pune, Maharashtra, 412207, India

Abstract: This work proposes a novel sophisticated mathematical model for the device. Initially, pre-acquired experimental data are collected, and a database is constructed. A variety of factors, such as gate length, electric field, gate width, drain-source voltage, and gate-source voltage, are included in the previously obtained experimental data. The B-Spline interpolation technique is used for the data augmentation process. An improved MobileNet model that defines three distinct models is then used to train and evaluate the supplemented data. The improved MobileNet model helps the network improve the training speed and accuracy. The training is executed iteratively till a minimal error is obtained between actual and predicted values. This work improves predictive accuracy by utilising experimental data enabling more dependable simulations that are essential for enhancing device performance and design. This extends beyond improved FinFET modelling; it sets a precedent for integrating advanced machine learning techniques in semiconductor technology.

Keywords: FinFET characterisation; B-Spline interpolation; improved MobileNet architecture; short channel effects; depth-wise separable convolution.

DOI: 10.1504/IJES.2025.149255

International Journal of Embedded Systems, 2025 Vol.18 No.2, pp.138 - 153

Received: 13 Jul 2024
Accepted: 01 Aug 2025
Published online: 20 Oct 2025 *

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