Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design (TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects. However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors (MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.

基于机器学习的代理模型在计算效率方面具有显着的优势。在本文中，我们提出了使用机器学习技术进行快速校准的试点研究。技术计算机辅助设计（TCAD）是一种强大的电子设备仿真工具。该模拟工具已广泛应用于辐射效应的研究。然而，TCAD 模型的校准非常耗时。在本研究中，我们介绍了一种用于金属氧化物半导体场效应晶体管 (MOSFET) 的 TCAD 模型校准的快速校准方法。该方法利用基于机器学习的代理模型，该模型比原始 TCAD 模拟快几个数量级。几秒钟内即可获得所需的校准结果。在本研究中，引入了包含 26 个参数的基本模型来表示 MOSFET 的典型结构。开发分类是为了提高训练样本生成的效率。采用特征选择技术来识别重要参数。建立了由分类器和回归器组成的代理模型。提出了基于替代模型的校准程序，并用三个校准目标进行了测试。我们的工作证明了基于机器学习的 MOSFET 快速模型校准的可行性。此外，这项研究表明，这些机器学习技术从数据中学习模式和相关性，而不是利用领域专业知识。这表明机器学习可以成为补充经典物理学研究的替代研究方法。