Review of Prognosis Approaches Applied to Power SiC MOSFETs for Health State and Remaining Useful Life Prediction

TL;DR

This paper reviews prognosis methods for predicting the health and lifespan of SiC MOSFETs, highlighting the use of data-based approaches and challenges in validation and uncertainty estimation.

Contribution

The paper provides a comprehensive review of prognosis tools for SiC MOSFETs, emphasizing data-based methods and their limitations.

Findings

Most studies use data-based methods, particularly neural networks, for SiC MOSFET prognosis.

Out-of-sample validation and uncertainty estimation are rarely addressed in the literature.

Degradation indicator trends significantly affect the performance of data-based prognosis methods.

Abstract

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.

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