Generation of Synthetic XPS spectra for Neural Network Quantification of RHEED Data of Complex Oxides

Neural networks are computational systems that rely on a series of weighted algorithms to processes input data and give an output. A common type of neural network used for image processing is a convolutional neural network (CNN). Due to their effectiveness at image classification, CNN’s have great potential to be useful in analysis of reflection high energy electron diffraction (RHEED) patterns of complex oxides. This potential is realized by creating a CNN that takes RHEED images as input and outputs a predicted x-ray phtotoelectron spectroscopy (XPS) spectrum of the material. Neural network performance depends on the weight values of the network, which are found by training the neural network. A problem that arises when training such a CNN is the limited availability of consistent XPS spectra to compare to the output of the neural network when training. This problem is overcome by using BriXias software to simulate a wide variety of XPS spectra. BriXias software utilizes a database of material characteristics to evaluate the inelastic mean free path (IMFP) and transport mean free path (TMFP) of electrons traveling within a material. It then uses the IMFP and TMFP, along with specified model parameters and XPS geometry, to simulate XPS data of material.