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Silicon carbide (SiC) MOSFET power modules are being used for high power applications because of their superior thermal characteristics and high blocking voltage capabilities over traditional silicon power modules. This paper explores modeling the thermal process of a SiC MOSFET power module through a high-order finite element analysis (FEA) based thermal model and then reducing the order of the FEA thermal model using a Krylov subspace method. The low-order thermal model has a significantly reduced computation cost compared to the FEA model while preserving the accuracy of the model. The proposed method is applied to generate low-order thermal models for a SiC MOSFET, which are validated by computer simulations with respect to the FEA thermal model.
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This content will become publicly available on March 16, 2026
Novel Approach of Determining and Predicting SiC MOSFET’s On Resistance From Device Case Temperature Using Machine Learning
Silicon-carbide (SiC) MOSFET devices are increasing in popularity in high-power converter applications. Device on-resistance (Rdson) is an important indicator for SiC MOSFET health status. Increments in Rdson over device lifetime indicate imminent device failure and result in decreased system efficiency. Direct and accurate measurement of SiC MOSFET device Rdson in high-power applications is difficult. Another approach is to estimate/predict the device Rdson from other, more easily measurable quantities, however, little work has been done on this approach in the literature. This leaves a significant technical gap in measuring/predicting device Rdson and slows down the device health status monitoring and power converter reliability research. To address the technical gaps, this work proposes a novel approach to predicting device Rdson from thermal cycle count and instantaneous temperature using machine learning regression models. The actual hardware data collected from accelerated lifetime tests of high-power SiC MOSFETs are used to train, test, and validate the proposed machine-learning regression models. The developed models, coupled with cycle counting algorithms, and device case thermal measurements, provide accurate live estimates of Rdson and can be used to predict changes in Rdson over expected mission profiles during power converter design.
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- Award ID(s):
- 2239169
- PAR ID:
- 10617217
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-1611-6
- Page Range / eLocation ID:
- 2393 to 2399
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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