PIPPON: Improve Impedance Prediction of Power Distribution Network Using Pole Proposal Network

While it is a difficult task to model and simulate a power distribution network’s (PDN) impedance profile for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup, it is a crucial process for the design and performance evaluation of the PDN. This paper proposes a new deep learning method PIPPON for PDN impedance prediction, which contains a proposal network specializing in predicting impedance profiles in the range around a pole point. The result shows that PIPPON produces more accurate results (with a 30 percent relative error reduction) than the previous deep learning method and maintains the same level of fast computation time as the previous method. Meanwhile, PIPPON focuses on impedance pole points resulting in a more accurate picture of whether the impedance profile meets the target impedance requirement.