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- Authors:
- Beladi, Seyedbehnam; Maurer, Linus; Stricker, Jonas; Pelz, Georg
- Document type:
- Konferenzbeitrag / Conference Paper
- Title:
- A Machine Learning Approach for Optimization of Automotive Power Switch Definition Space in Multi-Application Utilization
- Title of conference publication:
- 2024 International Semiconductor Conference (CAS)
- Conference title:
- International Semiconductor Conference (47., 2024, Sinaia)
- Venue:
- Sinaia, Romania
- Year of conference:
- 2024
- Date of conference beginning:
- 09.10.2024
- Date of conference ending:
- 11.10.2024
- Place of publication:
- Piscataway, NJ
- Publisher:
- IEEE
- Year:
- 2024
- Pages from - to:
- 199-202
- Language:
- 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
- Department:
- Fakultät für Elektrotechnik und Informationstechnik
- Institute:
- EIT 4 - Institut für Mikroelektronik und Schaltungstechnik
- Chair:
- Maurer, Linus
- Open Access yes or no?:
- Nein / No
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