Fast Impedance Prediction for Power Distribution Network using Deep Learning


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.


Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory

Publication Status

Early View: Online Version of Record before inclusion in an issue


This paper is based upon work supported by the National Science Foundation und er Grant No. IIP-1916535 and a Google Faculty Research Award.

Keywords and Phrases

Boundary Element Method; Deep Learning; Deep Neural Network; Impedance; Power Distribution Network

International Standard Serial Number (ISSN)

1099-1204; 0894-3370

Document Type

Article - Journal

Document Version


File Type





© 2021 Wiley, All rights reserved.

Publication Date

04 Oct 2021