Articles | Volume 21
https://doi.org/10.5194/ars-21-49-2023
https://doi.org/10.5194/ars-21-49-2023
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

Related authors

Iterative Placement of Decoupling Capacitors using Optimization Algorithms and Machine Learning
Zouhair Nezhi, Nima Ghafarian Shoaee, and Marcus Stiemer
Adv. Radio Sci., 21, 123–132, https://doi.org/10.5194/ars-21-123-2024,https://doi.org/10.5194/ars-21-123-2024, 2024
Short summary

Cited articles

Cecchetti, R., de Paulis, F., Olivieri, C., Orlandi, A., and Buecker, M.: Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning, Electronics, 9, 1243, https://doi.org/10.3390/electronics9081243, 2020. a
Chollet, F.: Keras, https://github.com/fchollet/keras (last access: 12 September 2023), 2015. a
Choy, D., Bartels, T., Stube, B., and Bruening, R.: A Contribution to the Treatment of Power Integrity Design Tasks using Reinforcement Learning, Adv. Radio Sci., 20, this issue, 2023. a
Franz, J.: EMV, Springer Fachmedien Wiesbaden GmbH, https://doi.org/10.1007/978-3-8348-2211-6, 2013. a
Download
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.