@inproceedings{,
    author = {Beladi, Seyedbehnam; Maurer, Linus; Stricker, Jonas; Pelz, Georg},
    title = {A ML-Based Approach for Finding the Product Definition Space of Microelectronic Power Switches},
    editor = {},
    booktitle = {2024 27th International Symposium on Design & Diagnostics of Electronic Circuits & Systems (DDECS) : Kielce, Poland, 2024},
    series = {},
    journal = {},
    address = {Piscataway, NJ},
    publisher = {IEEE},
    edition = {},
    year = {2024},
    isbn = {},
    volume = {},
    number =  {},
    pages = {148-151},
    url = {},
    doi = {10.1109/DDECS60919.2024.10508900},
    keywords = {},
    abstract = {Designing power electronic switches in a timely manner is essential for a wide range of electrical applications. The challenge arises when determining the acceptable design parameters from the product definition space that lead to a functioning application. Creating a well-defined product definition space can help reduce design cycle time and minimize the risk of design failure or non-compliance with requirements. In response to this, we propose a machine learning-based framework to create this space with substantially less simulations in comparison to exhaustive and optimization-based methods. This is particularly beneficial in higher dimensions where running numerous simulations may not be feasible. We applied this approach to generic silicon power switches within a half-bridge motor drive application simulation, defining borders that closely match-above 98%-to the borders of the product definition space. In this use case, the need for simulations is reduced by a factor of five, while still meeting all operating conditions and requirements.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Elektrotechnik und Informationstechnik, EIT 4 - Institut für Mikroelektronik und Schaltungstechnik, Professur: Maurer, Linus},
    
    
}