Hillebrecht, TilTilHillebrechtWeber, TommyTommyWeberAlfert, JohannesJohannesAlfertSchuster, ChristianChristianSchuster2025-07-072025-07-072025-0529th IEEE Workshop on Signal and Power Integrity, SPI 2025979-8-3315-2061-8979-8-3315-2062-5https://hdl.handle.net/11420/56110Machine Learning (ML) applications need large quantities of data spanning the complete Printed Circuit Board (PCB) design space. This design space needs to be reduced to the subset of relevant PCB configurations, i.e. those yielding network parameters which are different from each other. For this purpose a preliminary design space analysis is carried out and pre-processing steps are presented to unify the application of ML in the Signal and Power Integrity domains and pave the way to a standardized PCB representation format.enDatabase | Machine Learning | Power Integrity | Printed Circuit Board | Signal IntegrityTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringConference Paper10.1109/SPI64682.2025.11014326Conference Paper