StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images

An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. - Highlights: • An efficient model-based method for quantitative electron microscopy is introduced. • Images are modelled as a superposition of 2D Gaussian peaks. • Overlap between neighbouring columns is taken into account. • Structure parameters can be obtained with the highest precision and accuracy. • StatSTEM, auser friendly program (GNU public license) is developed.