Work

An Accurate and Reliable Behavioral Modeling Technique for Fully Printed Vanadium Dioxide RF Switches Using Model Ensembling Approach

Year: 2025

Type: article

Abstract: This paper develops and showcases model ensembling based accurate, reliable and computer-aided design integrable behavioral modeling technique for the emerging fully printed Vanadium dioxide (VO2) based Radio Frequency (RF) switches. Initially, separate and independent models are trained using Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) gradient boosting frameworks. The hyperparameters of standalone XGBoost, CatBoost, and LightGBM based models are optimized using random search optimization coupled with cross-validation scheme. Then, weighted ensemble models are constructed by leveraging the optimally trained XGBoost, CatBoost, and LightGBM based models. It is vital to carefully calibrate the ensembling weights, therefore an optimization algorithm namely Tuna Swarm Optimization (TSO) is employed. Finally, all the developed models are tried and validated on standard regression tests including for the mean relative error across all operating temperature conditions. The proposed weighted ensemble models have achieved remarkable accuracy and efficiency in simulating the behavior of VO2RF switches.

Source: IEEE Access

DOI: 10.1109/access.2025.3591890

Authors: Saddam Husain, Bagylan Kadirbay, Mohammad Vaseem, Atif Shamim, Mohammad Hashmi

Institutions: Nazarbayev University, King Abdullah University of Science and Technology

Language: English

Topic: Transition Metal Oxide Nanomaterials

Subfield: Polymers and Plastics

Field: Materials Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075