Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 21
Articles | Volume 21
https://doi.org/10.5194/ars-21-49-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-21-49-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21
01 Dec 2023
 | 01 Dec 2023

Generating AI modules for decoupling capacitor placement using simulation

Nima Ghafarian Shoaee, Zouhair Nezhi, Werner John, Ralf Brüning, and Jürgen Götze

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Latest update: 20 Nov 2024
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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.
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