Quantification of stem images in high resolution sem for segmented and pixelated detectors

0551125 - ÚPT 2023 RIV CH eng J - Journal Article
Konvalina, Ivo - Paták, Aleš - Zouhar, Martin - Müllerová, Ilona - Fořt, Tomáš - Unčovský, M. - Materna Mikmeková, Eliška
Quantification of stem images in high resolution sem for segmented and pixelated detectors.
Nanomaterials. Roč. 12, č. 1 (2022), č. článku 71. E-ISSN 2079-4991
R&D Projects: GA TA ČR(CZ) TN01000008
Grant - others:AV ČR(CZ) StrategieAV21/6
Program: StrategieAV
Institutional support: RVO:68081731
Keywords : STEM segmented detector * pixelated detector * scanning electron microscopy * Monte Carlo simulations * ray tracing * quantitative imaging
OECD category: Electrical and electronic engineering
Impact factor: 5.3, year: 2022
Method of publishing: Open access
https://www.mdpi.com/2079-4991/12/1/71

The segmented semiconductor detectors for transmitted electrons in ultrahigh resolution scanning electron microscopes allow observing samples in various imaging modes. Typically, two standard modes of objective lens, with and without a magnetic field, differ by their resolution. If the beam deceleration mode is selected, then an electrostatic field around the sample is added. The trajectories of transmitted electrons are influenced by the fields below the sample. The goal of this paper is a quantification of measured images and theoretical study of the capability of the detector to collect signal electrons by its individual segments. Comparison of measured and ray-traced simulated data were difficult in the past. This motivated us to present a new method that enables better comparison of the two datasets at the cost of additional measurements, so-called calibration curves. Furthermore, we also analyze the measurements acquired using 2D pixel array detector (PAD) that provide a more detailed angular profile. We demonstrate that the radial profiles of STEM and/or 2D-PAD data are sensitive to material composition. Moreover, scattering processes are affected by thickness of the sample as well. Hence, comparing the two experimental and simulation data can help to estimate composition or the thickness of the sample.
Permanent Link: http://hdl.handle.net/11104/0326569