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Modeling and simulating a power distribution network (PDN) for printedcircuit boards (PCBs) with irregular board shapes and multi-layer stackup iscomputationally inefficient using full-wave simulations. This paper presents anew concept of using deep learning for PDN impedance prediction. A boundaryelement method (BEM) is applied to efficiently calculate the impedance forarbitrary board shape and stackup. Then over one million boards with differentshapes, stackup, IC location, and decap placement are randomly generated totrain a deep neural network (DNN). The trained DNN can predict the impedanceaccurately 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 100times faster than the BEM method and 5000 times faster than full-wavesimulations.

Fast PDN Impedance Prediction Using Deep Learning