Adaptive Sequential Particle Filtering for Online Identification of SiC MOSFET Aging State During Power Cycling Test

During power cycling tests, accurate online monitoring and identification of the aging state of SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) is crucial for failure precursor analysis and lifespan prediction of the devices. Due to the nonlinear voltage-current relationship of SiC MOSFETs and the effects of thermal-electrical coupling, the data obtained from online monitoring often fail to directly reflect their actual aging states. This article proposes an adaptive sequential particle filtering (AS-PF) method that takes into account the thermal-electrical coupling effects of SiC MOSFETs during power cycling tests, enabling accurate online identification and tracking of their aging process. The method extracts the aging state of the SiC MOSFET within a single power cycling test cycle using sequential PF and achieves rapid tracking of the aging process by adaptively adjusting its noise. This article validates that AS-PF can accurately track changes in key aging parameters through power cycling tests on a commercial 1200-V SiC MOSFET. This work enhances the richness of data from power cycling tests by real-time tracking of the aging state, which can effectively support online failure precursor analysis and the development of more accurate lifespan models for SiC MOSFETs. © 2025 IEEE.