Morten Schierholz

Morten Schierholz received his M.S. degree in electrical engineering from Hamburg University of Technology (TUHH) in 2019. He is pursuing the Ph.D. degree at the Institute of Electromagnetic Theory at TUHH. His focus is on the application of machine learning tools for decoupling strategies of PCB based PDNs, including the generation of training data which is collected in the SI/PI-Database.


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Data-Efficient Supervised Machine Learning Technique for Practical PCB Noise Decoupling

DesignCon 2023 Best Paper Award Winner

Design of PCB-based PDNs has become a challenge due to rising power consumption, lowering supply voltages, increasing integration density and design complexity. In this paper, we propose an algorithmic procedure using supervised machine learning techniques to provide expert guidance on the PDN design and optimize power supply decoupling capacitors. The proposed method replaces the computationally expensive numerical simulations with faster ANNs.

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