TY  - EJOU
AU  - Manaz, Mohammed Ansar Mohammed 
AU  - Hong, Shang Ping 
AU  - Lee, Tzung-Lin 

TI  - A Unified Physics-of-Failure Framework for Reliability Prediction of SiC MOSFET Inverters under Stochastic Mission Profiles
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly account for the long-term device parameter variation of new technologies. This paper proposes a versatile model-based approach to study the SiC MOSFET based three-phase inverters under various mission profiles. The proposed framework combines electrical, thermal, and aging (ET&A) models to emulate the SiC MOSFET device parameter variations and their amplifying effect on the aging phenomenon. Unlike existing approaches, the proposed framework captures the coupled electrothermal–aging feedback and progressive parameter variation under stochastic mission profiles, enabling more accurate long-term reliability prediction. The model can be adopted in Monte-Carlo simulations to predict the reliability of three-phase inverters under application-specific stochastic mission profiles. The accuracy of the aging models is tested by comparing the results against accelerated lifetime test data obtained from the literature. An example case study is presented to demonstrate the application of the proposed ET&A models to predict the reliability of wind turbine inverters considering mission profile data from a wind farm site in northwest Taiwan.
KW  - Failure mechanisms; on-state resistance aging; reliability prediction; SiC MOSFET; three-phase inverter

DO  - 10.32604/cmes.2026.083270