Determination of thickness refinement using STEM detector segments

0524885 - ÚPT 2021 RIV CZ eng C - Conference Paper (international conference)
Skoupý, Radim - Krzyžánek, Vladislav
Determination of thickness refinement using STEM detector segments.
10th Anniversary International Conference on Nanomaterials - Research and Application (NANOCON 2018). Ostrava: Tanger, 2019, s. 677-681. ISBN 978-80-87294-89-5.
[Anniversary International Conference on Nanomaterials - Research and Application (NANOCON) /10./. Brno (CZ), 17.10.2018-19.10.2018]
R&D Projects: GA ČR GA17-15451S; GA MŠMT(CZ) LO1212; GA MŠMT ED0017/01/01
Institutional support: RVO:68081731
Keywords : electron-microscopy * Quantitative STEM * thickness determination * detector segments * Monte Carlo simulation
OECD category: Electrical and electronic engineering

Quantitative STEM imaging together with Monte Carlo simulations of electron scattering in solids can bring interesting results about properties of many thin samples. It is possible to determine thickness of a sample, to calculate mass of particles and measure mass per length/area. Appropriate calibration is one of the crucial parts of the method. Even a small error or inaccuracy in detector response to electron beam either blanked or full brings significant error into thickness determination. This problem can be overcome by parallel STEM imaging in more segments of the detector. Comparing more segments gives a possibility to use a signal from different segments for different thicknesses of a sample. Accuracy of individual parts of the detector depends on the captured signal quantity. It is desirable to use such a STEM detector segment that provides the greatest signal change to a unit of thickness. To demonstrate the usage, we used a sample of Latex nanospheres placed on thin carbon lacey film, diameter of the nanospheres was around 600 nm in order to compare the results from different detector segments. Thanks to the known thickness of the sample (calculated from its geometrical shape), it is possible to estimate the optimal acquisition settings and post processing steps with the known and the true state of the sample.
Permanent Link: http://hdl.handle.net/11104/0309122