LARGE-SIGNAL MODELING OF GAN HEMTS USING HYBRID GA-ANN, PSO-SVR, AND GPR-BASED APPROACHES
Loading...
Date
2020-11-03
Authors
Jarndal, Anwar
Husain, Saddam
Hashmi, Mohammad S.
Ghannouchi, Fadhel M.
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Journal of the Electron Devices Society
Abstract
This article presents an extensive study and demonstration of efficient electrothermal largesignal 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.
Description
Keywords
ANN modeling, GaN HEMT, GPR modeling, large-signal modeling, SVR modeling
Citation
Saddam Husain, Ahmad Khusro, Mohammad Hashmi, Galymzhan Nauryzbayev, Muhammad Akmal Chaudhary, "Demonstration of CAD Deployability for GPR Based Small-Signal Modelling of GaN HEMT", 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5, 2021.