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Keynote Lecture

Adaptive Scanning in Electron Microscopy

Thursday (27.09.2018)
18:00 - 18:30 S1/01 - A04
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In scanning electron microscopy, the achievable image quality is often limited by a maximum feasible acquisition time per dataset. Particularly with regard to three-dimensional or large field-of-view imaging, a compromise must be found between a high amount of shot noise, which leads to a low signal-to-noise ratio, and excessive acquisition times. 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. We thus propose a new recording method for SEM that drastically reduces the image acquisition time without sacrificing image quality. It applies in a similar manner to scanning transmission electron microscopy (STEM).

 

Speaker:
Additional Authors:
  • Christoph Pauly
    Saarland University
  • Michael Engstler
    Saarland University
  • Prof. Dr. Niels de Jonge
    INM Leibniz Institute for New Materials
  • Prof. Dr. Frank Mücklich
    Saarland University