Electronic Design Flow Improvement with Machine Learning Tools
Funding: Freie und Hansestadt Hamburg
Contact: Morten Schierholz, M.Sc.
01.10.2021 – 31.10.2024
The design of modern printed circuit boards (PCBs) is a challenging task and requires the compliance with a variety of specifications. To reduce the risk of a poor design and getting the desired functionality, the design processs is accompanied by many electromagnetic simulations. In combination with the time requirement of an individual simulaion this results in a large effort which needs a lot of computational resources. Because future designs will have a higher complexity and larger integration level, decreasing the time requirement for an individual simulation is an important task to solve. Without improving the simulation mechanism the design ow will require more time which results in additional costs for the development. This project aims to improve the design ow by providing a more efficient design tool and process. In the area of machine learning algorithms some promising ideas are found. Publications in recent years provide methods to increase the efficiency of optimization processes – for example generic algorithms for the placement of decoupling capacitors. With artificial neural networks first results are achieved by investigating the impedance of the power delivery network under the in uence of decoupling capacitors. Future work shall increase the applicability of artificial neural networks to a wider range of simulation tasks. Therefore different aspects have to be investigated. The focus is on printed circuit boards which are commonly used in many electronic devices. The functionality and capabilities of printed circuit boards is well understood. One of the most important aspects within this project is to provide not only a faster simulation ow but to ensure the consistency of simulation results with existing tools and methods.
Publications:
Generation and Application of a Very Large Dataset for Signal Integrity Via Array and Link Analysis Journal Article In: IEEE Transactions on Electromagnetic Compatibility, Early Access, pp. 1 -10, 2024. |
Dimensional Reduction by Auto-Encoders in Machine Learning Based Power Integrity Analysis Proceedings Article Forthcoming In: 2024 IEEE 28th Workshop on Signal and Power Integrity (SPI), Lisbon, Portugal, May 12-15, Forthcoming. |
Application of Gaussian Process Regression for Data Efficient Prediction of PCB-based Power Delivery Network Impedance Features Proceedings Article In: 2024 IEEE 28th Workshop on Signal and Power Integrity (SPI), Lisbon, Portugal, May 12-15 2024. |
PCB based Power Delivery Network Analysis Using Transfer Learning and Artificial Neural Networks Proceedings Article In: 2024 IEEE 28th Workshop on Signal and Power Integrity (SPI), Lisbon, Portugal, May 12-15, 2024. |
Applying Techniques of Transfer and Active Learning to Practical PCB Noise Decoupling Proceedings Article In: DesignCon 2024, Sanata Clara, USA, January 30 - February 1, 2024. |
Engineering-Informed Design Space Reduction for PCB Based Power Delivery Networks Journal Article In: IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 13, no. 10, pp. 1613 - 1623, 2023. |
Data-Efficient Supervised Machine Learning Technique for Practical PCB Noise Decoupling Proceedings Article In: DesignCon 2023 Early-Career, Santa Clara, CA, USA, January 30 - February 1, 2023. |
Parametric S-Parameters for PCB based Power Delivery Network Design Using Machine Learning Proceedings Article In: IEEE Workshop on Signal and Power Integrity (SPI), Siegen, Germany, May 22 -25, 2022. |
Evaluation of Support Vector Machines for PCB based Power Delivery Network Classification Proceedings Article In: IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), virtual event, Austin, TX, USA, October 17-20, 2021. |
ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification Proceedings Article In: IEEE Workshop on Signal and Power Integrity (SPI), virtual event, Siegen, Germany, May 10-12, 2021. |
Efficient and Flexible Huygens’ Source Replacement of mm-scale Human Brain Implants Proceedings Article In: Joint IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity (EMCS+SIPI) and Symposium on EMC Europe (EMC Europe), virtual event, Glasgow, Scotland, July 26 - August 20, 2021. |
SI/PI-Database of PCB-Based Interconnects for Machine Learning Applications Journal Article In: IEEE Access, vol. 9, pp. 34423-34432, 2021. |
ANN Performance for the Prediction of High-Speed Digital Interconnects over Multiple PCBs Proceedings Article In: IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) San Jose, CA, USA, 2020. |
Comparison of Collaborative versus Extended Artificial Neural Networks for PDN Design Proceedings Article In: IEEE Workshop on Signal and Power Intergrity (SPI) Cologne, Germany, May 17-20, 2020. |
Evaluation of Neural Networks to Predict Target Impedance Violations of Power Delivery Networks Proceedings Article In: IEEE Conference on Electrical Perfomance of Electronic Packaging and Systems (EPEPS) Montreal, Canada, October 6-9, 2019. |
