@inproceedings{c5ba7b7ec31542798762fef59a44ec44,
title = "A neural network-based manufacturing variability modeling of GaN HEMTs",
abstract = "A new technique to accurately model the manufacturing variability of GaN HEMT using a neural network(NN) is presented in this paper. Compact model parameters are automatically generated through Principal component analysis (PCA) parameters from variations in I-V data. Together with the bias conditions, the compact model parameters are used to train a neural network. The NN-based compact model captures the I-V behavior of 115 GaN HEMT with excellent accuracy. The trained neural network is converted to a standard Verilog-A file that can be imported to a circuit simulator. The NN-based compact model is further evaluated in terms of complexity and simulation speed. The presented technique shows great potential in developing a fast, flexible, and accurate NN-based compact model that can be applied to any device technology.",
author = "Fredo Chavez and Danial Bavi and Miller, {Nicholas C.} and Sourabh Khandelwal",
year = "2024",
doi = "10.1109/ICMTS59902.2024.10520695",
language = "English",
isbn = "9798350329902",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2024 IEEE 36th International Conference on Microelectronic Test Structures (ICMTS)",
address = "United States",
note = "36th IEEE International Conference on Microelectronic Test Structures, ICMTS 2024 ; Conference date: 15-04-2024 Through 18-04-2024",
}