用仿真数据训练神经网络，自动校准氧化镓器件关键参数。
Ga$_2$O$_3$ TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network
- 基于仿真数据训练自编码器与神经网络，实现参数自动校准。
- 五组关键参数从含噪实验曲线中提取，预开启区效果媲美专家。
- 引入物理约束后，全工作区间表现均达人工校准水平。
本文展示了仅使用TCAD仿真数据训练机器学习模型，实现半导体器件参数自动校准的可行性。针对新兴超宽禁带材料氧化镓（Ga2O3）制备的肖特基势垒二极管（SBDs），在不同有效阳极功函数（WF）和多种温度（T）下进行测量，获取其电流-电压特性曲线，并用于自动校准Ga2O3 Philips Unified Mobility（PhuMob）模型的五个关键参数。训练数据包含七种变量组合：WF、T及五个PhuMob参数。模型通过自编码器与神经网络构成，仅依赖仿真数据训练。随后，将该模型应用于含噪声的实验曲线，成功提取出参数。使用提取参数进行后续TCAD仿真表明，在预开启区域性能接近人工校准水平，但在开启状态区域较差；而引入简单物理信息神经网络（PINN）后，模型在所有工作区间表现均达到人工校准水准。
原文摘要 · Abstract (English)
In this paper, we demonstrate the feasibility of performing automatic Technology Computer Aided Design (TCAD) parameter calibration and extraction using machine learning, with the machine trained solely by TCAD simulation data. The methodology is validated using experimental data. Schottky Barrier Diodes (SBDs) with different effective anode workfunction (WF) are fabricated with emerging ultra-wide bandgap material, Gallium Oxide (Ga2O3), and are measured at various temperatures (T). Their current voltage curves are used for automatic Ga2O3 Philips Unified Mobility (PhuMob) model parameters calibration. Five critical PhuMob parameters were calibrated. The machine consists of an autoencoder and a neural network and is trained solely by TCAD simulation data with variations in WF, T, and the five PhuMob parameters (seven variations in total). Then, Ga2O3 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 pre-turned on regime, but not in the on state regime. By using a simple physics-informed neural network, the machine performs as well as the human expert in all regimes.
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