Generating AI modules for decoupling capacitor placement using simulation

Abstract

The effects of parameters affecting the input impedance of a power delivery network (PDN) are investigated. It is considered that the size of the power plane and the number of associated planes in the PCB layout, apart from the decoupling capacitor, have an effect on the impedance behavior within a certain frequency range. An artificial neural network (ANN) is trained using the generated data utilizing a process to generate suitable input for training a machine learning (ML) module, which is able to predict the impedance profile of the PDN. In order to obtain a more accurate prediction, Bayesian optimization is implemented and the results are compared to commercial power integrity (PI) software.