Smart Microscopy: Feature Based Adaptive Sampling for Focused Ion Beam Scanning Electron Microscopy

Tim Dahmen, Philipp Slusallek, Patrick Trampert, Frank Mücklich, Michael Engstler, Christoph Pauly, Niels de Jonge

In: Microscopy & Microanalysis - The Official M&M 2016 Proceedings. Microscopy & Microanalysis (M&M-2016) July 24-28 Columbus OH United States Seite 632 22 Suppl 3 Cambridge University Press 2016.


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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence