A semi-empirical approach to calibrate simulation models for semiconductor devices

• Published in: Scientific reports Vol. 13; no. 1; p. 10436
• Main Authors: Jaiswal, Rahul, Martínez-Ramón, Manel, Busani, Tito
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/4077/909/4101; 639/705/1042; 639/705/1046; Business metrics; Calibration; Datasets; Digital twins; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Photovoltaic cells; Photovoltaics; Prediction models; Science; Science (multidisciplinary); Silicon; Simulation; Solar cells
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-36196-z

Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. Ideally, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This calibration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non characterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device.