title: Feature Adaptive Sampling for Scanning Electron Microscopy.
abstract: A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-posteriori image processing techniques were applied to remove the resulting noise. The technique was realized by scanning the sample twice. The first, quick scan used small pixel-dwell times to generate a first, noisy image using a low electron dose. This image was analyzed automatically, and a software algorithm generated a sparse pattern of regions of the image that require additional sampling. A second scan generated a sparse image of only these regions, but using a highly increased electron dose. By applying a selective low-pass filter and combining both datasets, a single image was generated. The resulting image exhibited a factor of ≈3 better SNR than an image acquired with uniform sampling on a Cartesian grid and the same total acquisition time. This result implies that the required electron dose (or acquisition time) for the adaptive scanning method is a factor of ten lower than for uniform scanning.
authors.affiliation: German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Dahmen
authors.affiliation: Saarland University, 66123 Saarbrücken, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Engstler
authors.affiliation: Saarland University, 66123 Saarbrücken, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Pauly
authors.affiliation: German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany.|Saarland University, 66123 Saarbrücken, Germany.
authors.forename: Patrick
authors.initials: P
authors.lastname: Trampert
authors.affiliation: Saarland University, 66123 Saarbrücken, Germany.|INM-Leibniz Institute for New Materials, 66123 Saarbrücken, Germany.
authors.forename: Niels
authors.initials: N
authors.lastname: de Jonge
authors.affiliation: Saarland University, 66123 Saarbrücken, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Mücklich
authors.affiliation: German Research Center for Artificial Intelligence GmbH (DFKI), 66123 Saarbrücken, Germany.|Saarland University, 66123 Saarbrücken, Germany.
authors.forename: Philipp
authors.initials: P
authors.lastname: Slusallek