Abstract
The introduction of silicon carbide(SiC) has reduced the superiority of traditional silicon-based power module pack-aging strategies. As packaging strategies become increasingly complex, classical thermal modelling tools often prove inadequate in balancing efficiency with accuracy. Integrating these tools with machine learning (ML) can significantly enhance their application potential. This discussion commences by addressing the pressing issues in thermal modelling of SiC modules, specifically the challenges associated with multiple heat sources and heat spreading. During the design stage, ML models can swiftly simulate the thermal response of various packaging strategies, aiding engineers in eliminating ineffective options. In the monitoring phase, the employment of a digital twin enables a deeper investigation into degradation phenomena. This article reviews the current status and explores the potential applications of ML in thermal modelling of SiC power modules.
Original language | English |
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Title of host publication | Proceedings of the 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC) |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 979-8-3503-9036-0 |
ISBN (Print) | 979-8-3503-9037-7 |
DOIs | |
Publication status | Published - 2024 |
Event | 10th IEEE Electronics System-Integration Technology Conference, ESTC 2024 - Berlin, Germany Duration: 11 Sept 2024 → 13 Sept 2024 |
Conference
Conference | 10th IEEE Electronics System-Integration Technology Conference, ESTC 2024 |
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Country/Territory | Germany |
City | Berlin |
Period | 11/09/24 → 13/09/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- ML
- power module
- SiC
- Thermal modelling