Abstract
An efficient model-based estimation algorithm is introduced in order 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 the 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, is investigated. The highest attainable precision is reached even for low dose images. Furthermore, advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. 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
Nowadays, the field of nanotechnology requires more and more quantitative characterisations of nanomaterials
An efficient model-based estimation algorithm is introduced in order to quantify the atomic column positions and intensities from atomic resolution transmission electron microscopy ((S)TEM) images
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
Summary
The field of nanotechnology requires more and more quantitative characterisations of nanomaterials. In order to facilitate these needs, we developed a user-friendly software package called StatSTEM [7] This program allows to apply advanced image quantification methods , such as atom-counting, measuring atomic column positions, and strain-mapping, to atomic resolution electron microscopy images by using an efficient modelbased fitting algorithm. Model-based parameter estimation and the efficient model estimation algorithm For atomic resolution (scanning) transmission electron microscopy ((S)TEM) images, statistical parameter estimation theory provides an excellent tool to quantitatively extract unknown structure parameters [9, 10] This overlap is estimated in an iterative procedure by subtracting in each iteration step the estimated contributions of neighbouring columns from the small segment of the single atomic column
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