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Automated Image Acquisition for Low-Dose STEM at Atomic Resolution

Published online by Cambridge University Press:  23 May 2017

Andreas Mittelberger
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Christian Kramberger
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Christoph Hofer
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Clemens Mangler
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Jannik C. Meyer*
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
*
*Corresponding author. jannik.meyer@univie.ac.at
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Abstract

Beam damage is a major limitation in electron microscopy that becomes increasingly severe at higher resolution. One possible route to circumvent radiation damage, which forms the basis for single-particle electron microscopy and related techniques, is to distribute the dose over many identical copies of an object. For the acquisition of low-dose data, ideally no dose should be applied to the region of interest before the acquisition of data. We present an automated approach that can collect large amounts of data efficiently by acquiring images in a user-defined area-of-interest with atomic resolution. We demonstrate that the stage mechanics of the Nion UltraSTEM, combined with an intelligent algorithm to move the sample, allow the automated acquisition of atomically resolved images from micron-sized areas of a graphene substrate. Moving the sample stage automatically in a regular pattern over the area-of-interest enables the collection of data from pristine sample regions without exposing them to the electron beam before recording an image. Therefore, it is possible to obtain data with minimal dose (no prior exposure during focusing), which is only limited by the minimum signal needed for data processing. This enables us to minimize beam-induced damage in the sample and to acquire large data sets within a reasonable amount of time.

Type
Instrumentation and Software
Copyright
© Microscopy Society of America 2017 

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