Abstract: Peter G. Binev

University of South Carolina

Advanced Image Formation in Electron Microscopy

The scanning transmission electron microscope (STEM) receives information about the observed specimen based on the interactions of the electron beam with the kernels of the atoms in the specimen. The most common detection method in this regime is the high angle annular dark field (HAADF) in which the intensity of the response is approximately proportional to the square of the atomic numbers of the atoms. This effect is known as Z-square contrast and is used to obtain information about the crystalline structure of the specimen.

There are several random processes involved in obtaining a measurement: the distribution, both in space and time, of the electrons in the beam focused on the specimen; the fluctuations of the energies of the electrons; the fluctuations of the magnetic field of the electromagnetic lenses; the positions at the time of exposure of the atoms that are oscillating due to temperature; the amplification of the signal received at the detector; etc. In addition, there are several disturbances with unknown effects that can be considered as noise. The observed picture is appearing as a collection of blurred distributions around the positions of the atomic columns. The better localized the blurs, the more informative the image. However, it is often the case that such localization requires high amount of energy per square angstrom, which can damage the specimen.

Our goal is to find new approaches for data acquisition that can extract more relevant information and at the same time are less intrusive. Two main ideas will be discussed in the talk: the use of multiple low-dose scans from the same area and possible application of the compressed sensing setup.

This reported research is done in collaboration with Benjamin Berkels, Douglas Blom, Wolfgang Dahmen, Ronald DeVore, Philipp Lamby, Daniel Savu, Robert Sharpley, and Thomas Vogt.


Aachen Institute for Advanced Study in Computational Engineering Science (AICES)
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