Comprehensive Investigation and Comparative Analysis of Machine Learning-Based Small-Signal Modelling Techniques for GaN HEMTs
Husain S. Hashmi M. Ghannouchi F.M.
2022Institute of Electrical and Electronics Engineers Inc.
IEEE Journal of the Electron Devices Society
2022#101015 - 1032 pp.
A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these techniques rarely provide any inkling about their suitability in modeling GaN HEMTs under varied operating conditions. In this context, this paper thoroughly investigates various ML based techniques and identifies their suitability for specific application scenarios. At first, an array of commonly employed modeling techniques based around Artificial Neural Network, RANdom SAmple Consensus, Support Vector Regression, Gaussian Process Regression, Decision Tree, and Genetic algorithm assisted Artificial Neural Network are used for development of modeling framework to exploit the bias, frequency and geometry dependence on S-parameter based outputs. Subsequently, the ensemble techniques namely Bootstrap aggregating, Random Forests, Extremely Randomized Trees, AdaBoost, Gradient Tree Boosting, Histogram-based Gradient Boosting, and Extreme Gradient Boosting are also explored to understand the capability of these algorithms in the development of GaN HEMT small signal models. Thereafter, an exhaustive analysis of bias and variance is carried out to figure out the most appropriate algorithms for specific applications. The discrepancies during model development are removed by tuning the hyperparameters of the respective models using Random search optimization with 5-fold cross validation technique. Post tuning, the models are evaluated in terms of generalization capability, Advanced Design System compatibility, computational efficiency, training and simulation time, models capacity and parameterstuning time.
ANN , DT , ensemble methods , GA-ANN , GaN HEMT , GPR , RSO , small-signal modelling and SVR
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Nazarbayev University, School of Engineering and Digital Sciences, Department of Electrical and Computer Engineering, Nur-Sultan, 010000, Kazakhstan
University of Calgary, Department of Electrical and Computer Engineering, Calgary, T2N 1N4, Canada
Nazarbayev University
University of Calgary
10 лет помогаем публиковать статьи Международный издатель
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