A machine learning approach to TCAD model calibration for MOSFET

• Published in: Nuclear science and techniques 2023-12, Vol.34 (12), p.133-145, Article 192
• Main Authors: Wang, Bai-Chuan, Tang, Chuan-Xiang, Qiu, Meng-Tong, Chen, Wei, Wang, Tan, Xu, Jing-Yan, Ding, Li-Li
• Format: Article
• Language: English
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-023-01340-x

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.