Fast PDN Impedance Prediction Using Deep Learning

TL;DR

This paper introduces a deep learning approach to rapidly predict PDN impedance for complex PCB designs, significantly reducing computation time compared to traditional simulation methods.

Contribution

It presents a novel combination of boundary element method and deep neural network to efficiently and accurately predict PDN impedance for arbitrary PCB configurations.

Findings

DNN predicts impedance accurately for new configurations.

Prediction time is only 0.1 seconds, over 100 times faster than BEM.

Method outperforms full-wave simulations in speed by 5000 times.

Abstract

Modeling and simulating a power distribution network (PDN) for printed circuit boards (PCBs) 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, 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 seconds, which is over 100 times faster than the BEM method and 5000 times faster than full-wave simulations.