Research

GraphPCBS (graph neural networks for printed circuit boards) aims to use Graph Neural Networks to automatically optimize the schematic design of printed circuit boards. It is a joint project between the department of Intelligent Embedded Systems and the smart electronics-engineering company CELUS. Until now, the optimization takes a a lot of time even for an experienced engineer, resulting in either high development costs or suboptimal circuit designs that reduce product reliability and lifespan. In this project we combine forces in the fields of deep learning on graphs and electronic engineering to solve this problem.

• Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics