Accurate and Efficient Behavioral Modeling of GaN HEMTs Using An Optimized Light Gradient Boosting Machine
Husain S. Hashmi M. Ghannouchi F.M.
September 2025John Wiley and Sons Inc
Advanced Theory and Simulations
2025#8Issue 9
An accurate, efficient, and improved Light Gradient Boosting Machine (LightGBM) based Small-Signal Behavioral Modeling (SSBM) techniques are investigated and presented in this paper for Gallium Nitride High Electron Mobility Transistors (GaN HEMTs). GaN HEMTs grown on SiC, Si and diamond substrates of geometries 2 × 50 (Formula presented.), 10 × 200 (Formula presented.), and 4 × 125 (Formula presented.), respectively are used in this study. A versatile set of LightGBMs hyperparameters including learning and tree specific parameters are meticulously optimized using a modern and vigorous optimization algorithm namely Osprey Optimization Algorithm (OOA) with the objective to accomplish superior model performance. The developed OOA-LightGBM based models are validated for a wide array of operating conditions including for frequency values within a broad spectrum of 0.25 to 120 GHz, 0.1 to 26 GHz, and 0.1 to 40 GHz for GaN-on-SiC, GaN-on-Si, and GaN-on-Diamond HEMTs, respectively. The proposed SSBM techniques have demonstrated remarkable prediction ability and are impressively efficient for all the GaN HEMTs devices tested in this work.
GaN HEMTs , light gradient boosting machine (Lightgbm) , machine learning (ML) , osprey optimization algorithm (OOA) , small-signal behavioral modeling (SSBM)
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Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Department of Electrical and Computer Engineering
Department of Electrical and Computer Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026