Monte-Carlo Simulation in Electron Microscopy and Spectroscopy

Starý, V.

doi:10.5772/16122

Cited by 2 publications

(3 citation statements)

References 72 publications

(84 reference statements)

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“…The geometry of EBSD patterns is well understood and can be analyzed very rapidly using modern scanning electron microscopes; however, from a theoretical point of view, the formation of EBSD patterns poses a significant challenge: on the one hand, the generation of backscattered electrons (BSEs) is a stochastic process, typically modeled by means of Monte Carlo (MC) techniques (Heinrich et al, 1975; Joy, 1995; Stary, 2011). On the other hand, the BSEs undergo dynamic scattering processes on their way out of the sample; such interactions are typically modeled starting from the Darwin–Howie–Whelan dynamic equations (Howie & Whelan, 1961) in the Bloch wave formalism (Bloch, 1929; Humphreys, 1979), adapted for BSEs (Winkelmann et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations

2013

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A new approach for the simulation of dynamic electron backscatter diffraction (EBSD) patterns is introduced. The computational approach merges deterministic dynamic electron-scattering computations based on Bloch waves with a stochastic Monte Carlo (MC) simulation of the energy, depth, and directional distributions of the backscattered electrons (BSEs). An efficient numerical scheme is introduced, based on a modified Lambert projection, for the computation of the scintillator electron count as a function of the position and orientation of the EBSD detector; the approach allows for the rapid computation of an individual EBSD pattern by bi-linear interpolation of a master EBSD pattern. The master pattern stores the BSE yield as a function of the electron exit direction and exit energy and is used along with weight factors extracted from the MC simulation to obtain energy-weighted simulated EBSD patterns. Example simulations for nickel yield realistic patterns and energy-dependent trends in pattern blurring versus filter window energies are in agreement with experimental energy-filtered EBSD observations reported in the literature.

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Section: Introductionmentioning

confidence: 99%

“…is a stochastic process, typically modeled by means of Monte Carlo~MC! techniques~Heinrich et al, 1975;Joy, 1995;Stary, 2011!. On the other hand, the BSEs undergo dynamic scattering processes on their way out of the sample; such interactions are typically modeled starting from the Darwin-Howie-Whelan dynamic equations~Howie & Whelan, 1961!…”

Section: Introductionmentioning

confidence: 99%

Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations

2013

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“…In order to comprehend the image contrast formation in the above described different microscopic modes, a deeper understanding of these regimes is required and computer simulations are necessary. Since the Ulam’s invention and first von Neumann’s implementation of the Monte Carlo (MC) method, this simulation approach (for an introduction see [ 27 , 28 ]) spread over several scientific branches such as high-energy particle physics and astrophysics [ 29 ] or medical physics [ 30 ] and electron microscopy, see [ 31 , 32 , 33 , 34 ] and references therein. We have decided to simulate the passage of electrons through matter by employing Geant4 [ 35 ] open source platform with a wide user base and several modules usable for lower primary electron energies (up to tens of keV) in SEM and also a package [ 36 ] with a good description of even lower signal electron energies.…”

Section: Introductionmentioning

confidence: 99%

Quantification of STEM Images in High Resolution SEM for Segmented and Pixelated Detectors

et al. 2021

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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.

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Monte-Carlo Simulation in Electron Microscopy and Spectroscopy

Cited by 2 publications

References 72 publications

Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations

Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations

Quantification of STEM Images in High Resolution SEM for Segmented and Pixelated Detectors

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