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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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194909199 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mitigating Multiphysics Interference in Semiconductor Aging via Physics-Embedded Incremental Evolution. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Cheng%22">Yang, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cheng.yang@shiep.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zepeng%22">Liu, Zepeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Chao%22">Jiang, Chao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Liang%22">Xue, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Haoyang%22">Cui, Haoyang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2750. 27p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Reliability+of+electronics%22">Reliability of electronics</searchLink><br />*<searchLink fieldCode="DE" term="%22Prognostic+models%22">Prognostic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%2E+National+Aeronautics+%26+Space+Administration%22">United States. National Aeronautics & Space Administration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909199 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122750 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 2750 Subjects: – SubjectFull: Reliability of electronics Type: general – SubjectFull: Prognostic models Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Statistical models Type: general – SubjectFull: United States. National Aeronautics & Space Administration Type: general Titles: – TitleFull: Mitigating Multiphysics Interference in Semiconductor Aging via Physics-Embedded Incremental Evolution. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Cheng – PersonEntity: Name: NameFull: Liu, Zepeng – PersonEntity: Name: NameFull: Jiang, Chao – PersonEntity: Name: NameFull: Xue, Liang – PersonEntity: Name: NameFull: Cui, Haoyang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 12 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
