Use Case MLE I

Application and Chances of Machine Learning Methods in the Electromagnetic Compatibility

13.09.2022, 10:30 - 12:00
90 minutes
H 0.007


Algorithms form the basis of many software in the field of machine learning. How these algorithms work is usually hidden from the users of the software. This leads to the paradoxical situation that it is often unclear why a certain machine learning procedure works well or why it does not work well. It is often even unclear how the results are produced, resulting in data distortion and undesirable results with high negative costs. In this workshop we want to look under the hood of well-known machine learning methods such as linear regression, reinforcement learning, k-means clustering, decision trees and other methods and explain them using practical examples. At the end of the workshop, the participants should be able to decide for themselves which processes are best suited for their applications and how they can optimally tailor these processes to their needs.

Prof. Christian Schuster
Institut für Theoretische Elektrotechnik
Morten Schierholz
Institut für Theoretische Elektrotechnik


A. Sánchez-Masís, ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification. In 2021 IEEE 25th Workshop on Signal and Power Integrity (SPI), Siegen, Germany, May 2021

M. Schierholz et al., SI/PI-Database of PCB-Based Interconnects for Machine Learning Applications. IEEE Access, vol. 9, pp. 34423–34432, 2021. K. Scharff, C. M. Schierholz, C. Yang, and C. Schuster, ANN Performance for the Prediction of High-Speed Digital Interconnects over Multiple PCBs. In 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, CA, USA, Oct. 2020.

C. M. Schierholz, K. Scharff, and C. Schuster, Evaluation of Neural Networks to Predict Target Impedance Violations of Power Delivery Networks. In 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Montreal, QC, Canada, Oct. 2019.

M. Schierholz, C. Yang, K. Roy, M. Swaminathan, and C. Schuster, Comparison of Collaborative versus Extended Artificial Neural Networks for PDN Design. In 2020 IEEE 24th Workshop on Signal and Power Integrity (SPI), Cologne, Germany, May 2020.

K. Scharff, H. M. Torun, C. Yang, M. Swaminathan, and C. Schuster, Bayesian Optimization for Signal Transmission Including Crosstalk in a Via Array. In 2020 International Symposium on Electromagnetic Compatibility - EMC EUROPE, Rome, Italy, Sep. 2020