Funding: Freie und Hansestadt Hamburg
Contact: Jan Heßling, M.Sc.
Start: 03.03.2025

In the current market, there is a growing demand for enhanced power integrity design in modern integrated circuits, driven by increasing data rates and lower supply voltages. The design cycle involves numerous electromagnetic simulations, which require substantial time and computational resources. The application of machine learning offers a potential speed enhancement for accurate simulation results. Additionally, the combination of physical knowledge and data-driven approaches can provide novel insights into the behavior of printed circuit boards. The rise of transformer architectures and the resulting success of large language models is investigated regarding their applicability in electronic designs. The goal of the project is to establish a machine learning assisted workflow for the design of printed circuit boards regarding the compliance to power integrity requirements.