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

Saddam Husain, Bagylan Kadirbay, Anwar Jarndal, Mohammad Hashmi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)559-572
Number of pages14
JournalIEEE Journal of the Electron Devices Society
Volume11
DOIs
Publication statusPublished - 2023

Keywords

  • ANN
  • device modelling
  • GaN HEMTs
  • MATLAB
  • Python and R

ASJC Scopus subject areas

  • Biotechnology
  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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