A Hybrid CNN–iTransformer Framework with Physics-Informed Regularization for Accurate MOSFET RUL Estimation

Accurate forecasting of MOSFET on-state resistance drift can support remaining useful life (RUL) estimation under thermal overstress. Existing methods seldom combine local degradation patterns, long-range feature interactions, and a failure-mode-specific prior in one forecasting framework. We propose a CNN–iTransformer model that uses on-state resistance drift as its health indicator and includes a finite-difference sensitivity constraint. On the four test records reported in this study, the average mean absolute error (MAE), root mean square error (RMSE), and relative error (RE) were 2.996 × 10−4 Ω, 3.988 × 10−4 Ω, and 3.110 × 10−3, respectively. For Test 37, the predicted RUL was 850 sampling steps versus 843 observed steps, producing a seven-step absolute error. These results concern within-record forecasting on one accelerated-aging dataset; generalization to unseen devices remains to be established.