A Prediction Method for Degradation of SiC MOSFET Based on SVMD + TCN + EKPF Model

Remaining useful life (RUL) prediction of power semiconductor devices plays a crucial role in reliability design and predictive maintenance of power control system. This article introduces a data-driven methodology on predicting the RUL of the gate oxide layer in silicon carbide (SiC) MOSFETs. Firstly, a power cycling platform is established to collect the time-varying curves of threshold voltage and construct an aging dataset. Then, the successive variational mode decomposition (SVMD) algorithm is employed to adaptively decompose the signal of gate threshold voltage, helping suppress measurement noise and fluctuations caused by operating conditions while retaining degradation features. Subsequently, a Temporal Convolutional Network (TCN) is adopted to capture temporal dependencies in the degradation sequence, thereby improving the characterization of gate oxide health status assessment. Finally, the extended Kalman particle filter (EKPF) is employed to estimate the degradation state and quantify the associated uncertainty by recursively fusing model predictions with real-time measurements. The proposed method integrates the adaptive signal decomposition capability of SVMD, the temporal feature extraction capability of TCN, and the uncertainty quantification capability of EKPF. Their complementary integration improves prediction accuracy and robustness in gate oxide degradation evaluation for SiC MOSFET.