  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.
