Mitigating Multiphysics Interference in Semiconductor Aging via Physics-Embedded Incremental Evolution.
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| Title: | Mitigating Multiphysics Interference in Semiconductor Aging via Physics-Embedded Incremental Evolution. |
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| Authors: | Yang, Cheng1 (AUTHOR) cheng.yang@shiep.edu.cn, Liu, Zepeng1 (AUTHOR), Jiang, Chao1 (AUTHOR), Xue, Liang1 (AUTHOR), Cui, Haoyang1 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2750. 27p. |
| Subject Terms: | *Reliability of electronics, *Prognostic models, *Machine learning, *Statistical models |
| Company/Entity: | United States. National Aeronautics & Space Administration |
| Abstract: | Remaining useful life (RUL) prediction for power semiconductor devices such as insulated-gate bipolar transistors (IGBTs) is central to reliable power-electronics operation, yet remains challenging because degradation is non-stationary and electro-thermal precursors are strongly coupled. Here, we propose a physics-informed incremental learning framework (PIILF), which models aging as a latent incremental state-evolution process rather than static trajectory fitting. PIILF integrates an incremental state evolution network (ISEN) for state-wise degradation updates, task-adaptive parameter sharing (TAPS) for mitigating cross-task interference among coupled precursors, and a physics-informed observation decoder (PIOD) that reconstructs observables through electro-thermal coupling relations. On the NASA IGBT accelerated aging dataset, evaluated over 100 random seeds, PIILF achieves lower RMSE and MAE than TimesNet, TimeXer, and DeepHPM, while retaining competitive MAPE, a slightly better R 2 , and higher parameter efficiency. When the training data are reduced to 50% and 25%, PIILF exhibits smaller error increases than the baselines, indicating greater robustness in data-scarce settings. These findings suggest that embedding physical consistency directly into incremental representation learning provides an effective and efficient route to robust semiconductor RUL prediction. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Remaining useful life (RUL) prediction for power semiconductor devices such as insulated-gate bipolar transistors (IGBTs) is central to reliable power-electronics operation, yet remains challenging because degradation is non-stationary and electro-thermal precursors are strongly coupled. Here, we propose a physics-informed incremental learning framework (PIILF), which models aging as a latent incremental state-evolution process rather than static trajectory fitting. PIILF integrates an incremental state evolution network (ISEN) for state-wise degradation updates, task-adaptive parameter sharing (TAPS) for mitigating cross-task interference among coupled precursors, and a physics-informed observation decoder (PIOD) that reconstructs observables through electro-thermal coupling relations. On the NASA IGBT accelerated aging dataset, evaluated over 100 random seeds, PIILF achieves lower RMSE and MAE than TimesNet, TimeXer, and DeepHPM, while retaining competitive MAPE, a slightly better R 2 , and higher parameter efficiency. When the training data are reduced to 50% and 25%, PIILF exhibits smaller error increases than the baselines, indicating greater robustness in data-scarce settings. These findings suggest that embedding physical consistency directly into incremental representation learning provides an effective and efficient route to robust semiconductor RUL prediction. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19122750 |
