Data-Driven Design and Analysis of High-Speed Interconnects on PCBs
Funding: Freie und Hansestadt Hamburg
Contact: Til Hillebrecht, M.Sc.
Start date: 17.10.2022
The expansion of information networks such as the Industrial Internet of Things (IoT) is creating an ever-increasing demand for data. As a logical consequence, this creates a demand for data generation, processing, storage and data transport between these steps. This project is investigating ways of ensuring fast data transport. The aim is to use machine learning (ML) methods to accelerate the design process for high-speed connections. The main focus is on the structure of the PCB, but the effects of line coding and equalizers are also considered. The overall aim is to create a large SI/PI database on the basis of which a hybrid design flow can be created that combines data-driven and physics-based methods. It will be investigated for which sub-areas and in which way the respective ML methods can be used optimally.
Publications:
Automated Generation and Correlation of Physics-Based Via Models with Full-Wave Simulation for an SI/PI Database Proceedings Article In: Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Milpitas, CA, USA, October 15-18, 2023. |
Multiconductor Transmission Lines for Orbital Angular Momentum (OAM) Communication Links Journal Article In: IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 12, no. 2, pp. 329-340, 2022. |
Prediction of Frequency Dependent Shielding Behavior for Ground Via Fences in Printed Circuit Boards Proceedings Article In: IEEE Workshop on Signal and Power Integrity (SPI), Chambery, France, June 18-21, 2019. |
