Methods and systems for x-ray scatterometry measurements employing a machine learning based electromagnetic response model

Abstract

Methods and systems for estimating values of parameters of interest from X-ray scatterometry measurements with reduced computational effort are described herein. Values of parameters of interest are estimated by regression using a trained, machine learning (ML) based electromagnetic (EM) response model. A training data set includes sets of Design Of Experiments (DOE) values of parameters of interest and corresponding DOE values of a plurality of electromagnetic response metrics. In some examples, values of parameters of interest are determined from measured images based on regression using a sequence of trained ML based electromagnetic response models. In some examples, input values employed to train the ML based EM response model are scaled based on model output variation.