@inproceedings{c015249c79c34da0a9ccd30a45c0d310,
title = "Artificial Neural Network-Based (ANN) Approach for Characteristics Modeling and Prediction in GaN-on-Si Power Devices",
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.",
keywords = "Artificial neural network, GaN-on-Si, MIS-HEMTs, Modeling, Prediction, p-GaN HEMTs",
author = "Kutub, {Sayeem Bin} and Jiang, {Hong Jia} and Chen, {Nan Yow} and Lee, {Wen Jay} and Jui, {Chia Yung} and Wu, {Tian Li}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd International Symposium on Power Semiconductor Devices and ICs, ISPSD 2020 ; Conference date: 13-09-2020 Through 18-09-2020",
year = "2020",
month = sep,
doi = "10.1109/ISPSD46842.2020.9170110",
language = "English",
series = "Proceedings of the International Symposium on Power Semiconductor Devices and ICs",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "529--532",
booktitle = "Proceedings of the 2020 32nd International Symposium on Power Semiconductor Devices and ICs, ISPSD 2020",
address = "美國",
}