Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

IEEE Journal of the Electron Devices Society

Fadhel M. Ghannouchi¹  Saddam Husain²  Anwar Jarndal²  Mohammad Hashmi³ 

[1] Department of Electrical and Computer Engineering, IRadio Lab, University of Calgary, Calgary, Canada;Electrical Engineering Department, University of Sharjah, Sharjah, UAE;Electrical and Computer Engineering Department, Nazarbayev University, Nur-Sultan, Kazakhstan;

关键词: ANN modeling;    GaN HEMT;    GPR modeling;    large-signal modeling;    SVR modeling;   

DOI  :  10.1109/JEDS.2020.3035628

来源: DOAJ

【 摘 要 】

This article presents an extensive study and demonstration of efficient electrothermal large-signal GaN HEMT modeling approaches based on combined techniques of Genetic Algorithm (GA) with Artificial Neural Networks (ANN), and Particle Swarm optimization (PSO) with Support Vector Regression (SVR). Another promising Gaussian Process Regression (GPR) based large-signal modeling approach is also explored and presented. The GA-ANN addresses the typical problem of local minima associated with the backpropagation (BP) based ANN. The GA successfully aids in the determination of optimal initial values for BP-ANN and enables it to find a unique optimal solution after subsequent of iterations with higher rate of convergence. This is also achieved using PSO-SVR with lower optimization variables. The developed modeling techniques are demonstrated and used to simulate the gate and drain currents of a 2-mm GaN device. All the models are relatively simple, practical, and easy to implement. The gate and drain currents models are embedded in an equivalent large-signal circuit’s model and built in Advanced Design System (ADS) software. The implemented model is validated by large-signal measurements and very good fitting results have been obtained. The model also showed an accurate simulation for a nonlinear power amplifier with very good computational speed and convergence.

【 授权许可】

Unknown