Artificial Neural Network-Based (ANN) Approach for Characteristics Modeling and Prediction in GaN-on-Si Power Devices

Sayeem Bin Kutub, Hong Jia Jiang, Nan Yow Chen, Wen Jay Lee, Chia Yung Jui, Tian Li Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper reports on the demonstration of the characteristics modeling and prediction in GaN-on-Si power devices (MIS-HEMTs and p-GaN HEMTs) using the artificial neural network (ANN)-based approach. A multi-layer ANN is developed to model the electrical characteristics, e.g., V$_{TH}, {I}_{D} V_{G}$, hysteresis, breakdown characteristics, and time-dependent dielectric breakdown (TDDB), etc. Furthermore, an autoencoder with two ANNs is also developed to reconstruct the device designs based on the specific characteristics. We show that the ANN-based approach is promising for modeling and prediction with multidimensional parameters, further assisting in the optimization for GaN-based devices towards the targeted performance.

Original languageEnglish
Title of host publicationProceedings of the 2020 32nd International Symposium on Power Semiconductor Devices and ICs, ISPSD 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages529-532
Number of pages4
ISBN (Electronic)9781728148366
DOIs
StatePublished - Sep 2020
Event32nd International Symposium on Power Semiconductor Devices and ICs, ISPSD 2020 - Virtual, Online, Austria
Duration: 13 Sep 202018 Sep 2020

Publication series

NameProceedings of the International Symposium on Power Semiconductor Devices and ICs
Volume2020-September
ISSN (Print)1063-6854

Conference

Conference32nd International Symposium on Power Semiconductor Devices and ICs, ISPSD 2020
CountryAustria
CityVirtual, Online
Period13/09/2018/09/20

Keywords

  • Artificial neural network
  • GaN-on-Si
  • MIS-HEMTs
  • Modeling
  • p-GaN HEMTs
  • Prediction

Fingerprint Dive into the research topics of 'Artificial Neural Network-Based (ANN) Approach for Characteristics Modeling and Prediction in GaN-on-Si Power Devices'. Together they form a unique fingerprint.

Cite this