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Comprehensive Investigation of ANN Algorithms Implemented in MATLAB, Python, and R for Small-Signal Behavioral Modeling of GaN HEMTs

Authors :
Saddam Husain
Bagylan Kadirbay
Anwar Jarndal
Mohammad Hashmi
Source :
IEEE Journal of the Electron Devices Society, Vol 11, Pp 559-572 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

Artificial Neural Network (ANN) is frequently utilized for the development of behavioral models of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs). However, exhaustive investigation concerning the ANN algorithms implemented in major programming platforms for small-signal behavioral models of GaN HEMTs is generally not available. To fill this void, this paper carefully examines and evaluates ANN algorithms implemented in MATLAB, Python and R software environments for the development of accurate and efficient GaN HEMTs modelling. At first, the ANN based models are developed using MATLAB, Python’s major frameworks namely Keras, PyTorch and Scikit-learn, and R’s ANN framework namely H2O to model the GaN devices. Thereafter, an in-depth analysis is carried out to comprehend the usefulness of each framework in different application scenarios. At last, a detailed evaluation of the developed models in terms of generalization capability, training and prediction speed, seamless integration with the standard circuit design tool advanced design system, and of the development environments in respect of support and documentation, user-friendly interface, ease of model development, open-access and cost is carried out.

Details

Language :
English
ISSN :
21686734
Volume :
11
Database :
Directory of Open Access Journals
Journal :
IEEE Journal of the Electron Devices Society
Publication Type :
Academic Journal
Accession number :
edsdoj.f114c2a8e4234c5e8a202f02433ccdb7
Document Type :
article
Full Text :
https://doi.org/10.1109/JEDS.2023.3324084