Generating AI modules for decoupling capacitor placement using simulation
Nima Ghafarian Shoaee
CORRESPONDING AUTHOR
Information Processing Lab, Technical University of Dortmund, Dortmund, Germany
Zouhair Nezhi
Theoretical Electrical Engineering and Numerical Field Computation, Helmut Schmidt University, Hamburg, Germany
Werner John
Pyramide 2525/TU Dortmund, Doerener Weg 4B, 33100 Paderborn, Germany
Ralf Brüning
EMC Technology Center/Zuken GmbH, Am Hoppenhof 30, 33104 Paderborn, Germany
Jürgen Götze
Information Processing Lab, Technical University of Dortmund, Dortmund, Germany
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Short summary
The effects of parameters (decaps, power plane and stackup) affecting the input impedance of a power delivery network are investigated. An artificial neural network is trained using the generated data utilizing a process to generate suitable input for training a machine learning module, which is able to predict the impedance profile of the PDN. In order to obtain a more accurate prediction, Bayesian optimization is implemented and the results are compared to commercial power integrity software.
The effects of parameters (decaps, power plane and stackup) affecting the input impedance of a...