Machine Learning Framework for Power Delivery Network Modelling

Sourav, Soumya; Roy, A.; Cao, Y.; Pandey, Shree

doi:10.1109/emcsi38923.2020.9191530

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…In recent years, the success of deep learning for complex and non-linear problems like computer vision, 6 natural language processing, 7 and strategy games 8 has also impacted many other fields. There has been some research [9][10][11][12] in applying machine learning in PDN modeling and optimization. However, most of these works do not have a welltrained and generalized machine learning model for PDN impedance prediction at the PCB level.…”

Section: Introductionmentioning

confidence: 99%

Fast impedance prediction for power distribution network using deep learning

Zhang

Juang

Kiguradze

et al. 2021

Int J Numerical Modelling

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Modeling and simulating a power distribution network (PDN) for printed circuit boards with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept of using deep learning for PDN impedance prediction. A boundary element method (BEM) is applied to efficiently calculate the impedance for arbitrary board shape and stackup. Then over one million boards with different shapes, stackup, integrated circuits (IC) location, and decap placement are randomly generated to train a deep neural network (DNN). The trained DNN can predict the impedance accurately for new board configurations that have not been used for training. The consumed time using the trained DNN is only 0.1 s, which is over 100 times faster than the BEM method and 10 000 times faster than full-wave simulations.

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