Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis

Khusro A. Husain S. Hashmi M.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2025 #13 136483 - 136504 pp.

Precise modeling of gallium nitride (GaN) high-electron mobility transistors (HEMTs) is vital for ensuring reliable and scalable RF circuit design, and efficient characterization of the device behavior. This article presents robust hybrid equivalent circuit (EC)–machine learning (ML) frameworks for significantly streamlining the extraction of small-signal model parameters of AlGaN/GaN HEMTs. The extrinsic and intrinsic parameters of the devices are initially extracted using physics-relevant empirical models in Keysight’s advanced design system. Thereafter, six extensively optimized ML regression models, namely decision tree (DT), ensemble learning (EL), support vector regression (SVR), kernel approximation regression (KAR), Gaussian process regression (GPR), and neural networks (NN) are employed to simulate the intrinsic behavior of GaN HEMTs. The models are trained on GaN HEMTs of geometries 4 × 100 µm, 10 × 220 µm, and 10 × 250 µm, while tested on GaN HEMTs of geometries 2 × 200 µm and 10 × 200 µm across diverse biasing and frequency conditions. The input features to models include gate–source voltage (V_GS), drain–source voltage (V_DS), frequency, number of fingers (NF), unity gate width (W_g), and effective gate width (W_eff). Finally, a thorough quantitative assessment and detailed comparisons are performed in terms of standard regression tests, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, training and prediction speed, reliability of model parameters, and simulation agreement with the measured S-parameters. The results demonstrate that among the tested ML models, EL exhibited the lowest mean relative S-parameter errors (2.78–3.75 %), followed by NNs (2.05–6.98 %), DT (2.29–7.35 %), GPR (2.86-8.91 %) SVR (7.83–9.88 %), and KAR (8.26–10.54 %) across diverse GaN HEMTs geometries. This hybrid modeling strategy provides a practical alternative to conventional parameter extraction, offering speed, accuracy, and broader applicability.

AlGaN/GaN HEMT , data-driven design , hybrid modeling , intrinsic parameter extraction , machine learning , RF transistor modeling , small-signal modeling

Text of the article Перейти на текст статьи

Nazarbayev University, School of Engineering and Digital Sciences, Department of Electrical and Computer Engineering, Astana, 010000, Kazakhstan

Nazarbayev University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026