Low-energy electron microscopy intensity-voltage data -- factorization, sparse sampling, and classification

TL;DR

This paper introduces FSC3, an unsupervised, fast factorization algorithm for LEEM I-V data that enables efficient surface phase identification, sparse sampling, and supervised classification, significantly reducing measurement time.

Contribution

The paper presents FSC3, a novel unsupervised factorization method for LEEM I-V data, combined with sparse sampling and SVM classification for surface analysis.

Findings

FSC3 effectively separates spectral and concentration components in LEEM I-V data.

Sparse sampling reduces measurement time by 10-100 times.

Supervised classification accurately identifies surface phases using FSC3 features.

Abstract

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyze. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC3) for identifying distinct physical surface phases. Importantly, FSC3 is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC3 concentrations are providing the features for a support vector machine (SVM) based supervised classification of the types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both exemplary LEEM I-V datasets.