Review of Prognosis Approaches Applied to Power SiC MOSFETs for Health State and Remaining Useful Life Prediction
The use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodological synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the dependence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prognosis tools for SiC MOSFETs remains an ongoing research challenge.
Authors
- Malorie Hologne-Carpentier
- Bruno Allard
- François Auger (ORCID: https://orcid.org/0000-0001-9158-1784)
- Clerc Guy
- Sanjiv Kumar
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Centre National de la Recherche Scientifique (FR)
- ECAM School of Engineering (FR)
- Laboratoire Ampère (FR)
- Laboratoire de Génie des Procédés – Environnement – Agro-alimentaire (FR)
- Institut National des Sciences Appliquées de Lyon (FR)
- Nantes Université (FR)
Publication Details
- Journal
- Entropy
- Published
- 2026-02-17
- DOI
- https://doi.org/10.3390/e28020234
- Primary Topic
- Silicon Carbide Semiconductor Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Agence Nationale de la Recherche