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- Autorinnen/Autoren:
- Beladi, Seyedbehnam; Maurer, Linus; Stricker, Jonas; Pelz, Georg
- Dokumenttyp:
- Konferenzbeitrag / Conference Paper
- Titel:
- A Machine Learning Approach for Optimization of Automotive Power Switch Definition Space in Multi-Application Utilization
- Titel Konferenzpublikation:
- 2024 International Semiconductor Conference (CAS)
- Konferenztitel:
- International Semiconductor Conference (47., 2024, Sinaia)
- Tagungsort:
- Sinaia, Romania
- Jahr der Konferenz:
- 2024
- Datum Beginn der Konferenz:
- 09.10.2024
- Datum Ende der Konferenz:
- 11.10.2024
- Verlagsort:
- Piscataway, NJ
- Verlag:
- IEEE
- Jahr:
- 2024
- Seitenbereich:
- 199-202
- Sprache:
- Englisch
- Abstract:
- The design process of power switches usually has time constraints, relies on computationally extensive simulation models and is performed for each application separately. A well-defined product is more likely to lead to a more efficient design and reduce the time-to-market by checking for application compatibility and parameter optimization in advance. The product definition space encompasses the multidimensional parameter space where specifications, constraints, and trade-offs of a product's design and functionality are defined and optimized. This work aims at leveraging machine learning surrogate models to discover this space in minutes rather than weeks, provides at least 99% coverage of solution spaces while having < 2% Pareto front error.
- ISBN:
- 979-8-3503-5207-8
- DOI:
- 10.1109/CAS62834.2024.10736744
- Fakultät:
- Fakultät für Elektrotechnik und Informationstechnik
- Institut:
- EIT 4 - Institut für Mikroelektronik und Schaltungstechnik
- Professorin/Professor:
- Maurer, Linus
- Open Access:
- Nein / No
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