Lifetime Prediction of SiC MOSFET by LSTM Based on IGWO Algorithm

SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of adaptability, stability, and accuracy due to the complexity of the failure process in SiC MOSFET. This article proposes an improved grey wolf optimizer-based long short-term memory (IGWO-LSTM) model for SiC MOSFET lifetime prediction. The model introduces a Tent chaotic mapping to generate an initial population with optimal distribution, ensuring comprehensive search space coverage and enhancing dynamic search adaptability. Then, a nonlinear control parameter strategy and the principle of particle swarm optimization (PSO) are added. The feature extraction capability of the model is strengthened, and the exploration and exploitation phases are dynamically balanced. The optimizations enable faster discovery of the global optimum while maintaining solution quality, thereby improving prediction accuracy and stability. Finally, power cycling experiments were conducted on two types of SiC MOSFETs with different internal resistances to validate the effectiveness of the proposed model. The proposed IGWO-LSTM model achieves high prediction accuracy, with R2 values of 96.2%, 94.8%, 94.1%, and 93.9% for four SiC MOSFETs, and RMSE values as low as 0.0117, 0.0143, 0.0152, and 0.0158, respectively. This represents an average improvement in R2 by 16%, 8%, and 4%, and a reduction in RMSE by up to 67.03%, 50.39%, and 31.57% compared with other intelligent models. Similarly, IGWO-LSTM achieves reductions in MAE of approximately 68%, 50%, and 30%, with corresponding reductions in MAPE of about 70%, 48%, and 26%, respectively. The results demonstrate superior performance in prediction accuracy, stability, and adaptability of the proposed model.