Ga$_2$O$_3$ TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network

• URL: http://arxiv.org/abs/2504.02283v1
• Date: Thu, 03 Apr 2025 05:09:43 GMT
• Title: Ga$_2$O$_3$ TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network
• Authors: Le Minh Long Nguyen, Edric Ong, Matthew Eng, Yuhao Zhang, Hiu Yung Wong,
• Abstract summary: We show the possibility of performing automatic Technology Computer-Aided-Design (TCAD) parameter calibration using machine learning, verified with experimental data.<n>A machine comprised of an autoencoder (AE) and a neural network (NN) (AE-NN) is used.<n>TCAD extracted parameters shows that the quality of the parameters is as good as an expert at the pre-turned-on regime but not in the on-state regime.
• Score: 3.194221922047046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we demonstrate the possibility of performing automatic Technology Computer-Aided-Design (TCAD) parameter calibration using machine learning, verified with experimental data. The machine only needs to be trained by TCAD data. Schottky Barrier Diode (SBD) fabricated with emerging ultra-wide-bandgap material, Gallium Oxide (Ga$_2$O$_3$), is measured and its current-voltage (IV) is used for Ga$_2$O$_3$ Philips Unified Mobility (PhuMob) model parameters, effective anode workfunction, and ambient temperature extraction (7 parameters). A machine comprised of an autoencoder (AE) and a neural network (NN) (AE-NN) is used. Ga$_2$O$_3$ PhuMob parameters are extracted from the noisy experimental curves. TCAD simulation with the extracted parameters shows that the quality of the parameters is as good as an expert's calibration at the pre-turned-on regime but not in the on-state regime. By using a simple physics-informed neural network (PINN) (AE-PINN), the machine performs as well as the human expert in all regimes.

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