AI-Driven Digital Twin for Health Monitoring of Wide Band Gap Power Semiconductors
Alireza Mehrabi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Keyvan Yari (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Willem D. Van Driel (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Rene H. Poelma (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
A significant challenge in the implementation of health monitoring systems for estimating the health state of devices is the lack of accurate information about design details. This challenge is particularly prominent in the field of power electronics, where both IC designers and converter designers are often hesitant to share information about their designs. Addressing this issue, this paper introduces a novel AI-driven digital twin modeling methodology that enables the detection and classification of failures in power semiconductors, particularly Wide Band Gap semiconductors. By employing AI-based system identification techniques, this method offers a noninvasive approach to health monitoring of power switches with high resolution, even while operating under real conditions. The proposed method has been validated by simulating wire bond failure in a SiC power MOSFET using MATLAB SIMULINK, and the results demonstrate its accuracy.