This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited ‘expensive’ experimental data, ‘low cost’ TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga2O3) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000–10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation.

中文翻译：

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用于设备变化和工作温度分析的 TCAD-机器学习框架与实验演示

这项工作首次通过实验演示了 TCAD-机器学习 (TCAD-ML) 框架，以帮助分析设备到设备的变化和操作（环境）温度，而无需提取物理量。这项工作中使用的 ML 算法是主成分分析 (PCA)，然后是三阶多项式回归。在校准到有限的“昂贵”实验数据后，“低成本”TCAD 模拟用于生成大量设备数据来训练 ML 模型。然后使用 ML 从任何给定的实验电流-电压 (IV) 特性中确定器件变化和工作温度的根本原因。我们应用这个框架来研究超宽带隙氧化镓 (Ga2O3) 肖特基势垒二极管 (SBD)，一种新兴的设备技术，对恶劣环境中的温度传感、RF 和电源应用前景广阔。校准后，进行超过 150,000 次电热 TCAD 模拟，物理参数（阳极有效功函数、漂移层掺杂和漂移层厚度）和工作温度随机变化。使用这些 TCAD 数据训练 ML 模型，我们发现 1,000-10,000 个 TCAD 数据可以训练一台准确的机器。我们表明，在没有物理量提取的情况下，执行 PCA 对于 TCAD 训练的 ML 模型适用于分析实验特征至关重要。ML 模型预测的物理参数和温度与实验分析非常吻合。