Stitching accuracy in large area scanning probe microscopy

Abstract

Image stitching is a technique that can significantly enlarge the scan area of scanning probe microscope (SPM) images. It is also the most commonly used method to cover large areas in high-speed SPM. In this paper, we provide details on stitching algorithms developed specifically to mitigate the effects of SPM error sources, namely the presence of scanner non-flatness. Using both synthetic data and flat samples we analyse the potential uncertainty contributions related to stitching, showing that the drift and line mismatch are the dominant sources of uncertainty. We also present the 'flatten base' algorithm that can significantly improve the stitched data results, at the cost of losing the large area form information about the sample.Image stitching is a technique that can significantly enlarge the scan area of scanning probe microscope (SPM) images. It is also the most commonly used method to cover large areas in high-speed SPM. In this paper, we provide details on stitching algorithms developed specifically to mitigate the effects of SPM error sources, namely the presence of scanner non-flatness. Using both synthetic data and flat samples we analyse the potential uncertainty contributions related to stitching, showing that the drift and line mismatch are the dominant sources of uncertainty. We also present the 'flatten base' algorithm that can significantly improve the stitched data results, at the cost of losing the large area form information about the sample.