Authors: Eng-Jon Ong | Le Nguyen | Matthew Eng | Yuhao Zhang | Hiu Yung Wong
Source: IEEE Transactions on Electron Devices (preprint, 2026-01-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ga <sub>2</sub> O <sub>3</sub> TCAD Mobility Parameter Calibration Using Simulation Augmented Machine Learning With Physics-Informed Neural Network In this article, we demonstrate the feasibility of performing automatic technology computer-aided-design (TCAD) parameter calibration and extraction using machine learning (ML), with the machine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trained solely by TCAD-simulation data</i>. The methodology is validated using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">experimental data</i>. Schottky barrier diodes (SBDs) with different effective anode workfunctions (WF) are fabricated with emerging ultrawide bandgap material, Gallium Oxide (Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}_{{3}}\text {)}$</tex-math> </inline-formula>, and are measured at various temperatures (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}\text {)}$</tex-math> </inline-formula>. Their current-voltage (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$</tex-math> </inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$</tex-math> </inline-formula>) curves are used for automatic Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Philips unified mobility (PhuMob) model parameters calibration. Five critical PhuMob parameters (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\mu }_{\max },\boldsymbol {\mu } _{\min },{N}_{\text {ref}},\boldsymbol {\alpha }$</tex-math> </inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\theta } \text {)}$</tex-math> </inline-formula> were calibrated. The machine consists of an autoencoder (AE) and a neural network (NN) and is trained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">solely</i> by TCAD simulation data with variations in WF, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$</tex-math> </inline-formula>, and the five PhuMob parameters (seven variations in total). Then, Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> PhuMob parameters are extracted from the noisy experimental curves. Subsequent TCAD simulation using the extracted parameters shows that the quality of the parameters is as good as an expert’s calibration at the preturned-on regime, but not in the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small>-state regime. By using a simple physics-informed NN (PINN), the machine performs as well as the human expert in all regimes. Artificial neural network ; Calibration ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Statistical physics ; Quantum mechanics Ga2O3 and related materials