Thesis

Dynamical models for novel diffraction techniques in SEM

Creator
Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2020
Thesis identifier
  • T15507
Person Identifier (Local)
  • 201491324
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • The scanning electron microscope is a powerful nanocharacterisation tool for a variety of materials including semiconductors and metals. Less known for its diffraction abilities than its transmission counterpart, scanning electron microscopy (SEM)can be used in a number of diffraction modalities to provide information on crystal imperfections at the nanoscale level. This comes with the added benefit of SEM requiring minimal sample preparation. Models for diffraction in the SEM are still being developed and improved, hence in this work I explore the physics and implementations of such models. I focus on the two main branches of SEM diffraction techniques:incident beam channelling, or diffraction in, powerful when it comes to resolving individual dislocations close to the surface; and back(/forward)scattering diffraction,or diffraction out, which provides a variety of information about grain distribution,orientation and strain. Both of these diffraction modalities involve the same physical processes, so it makes sense to use the same models, namely dynamical scattering in the column approximation. I use the two beam Bloch waves approach for electron channelling contrast imaging (ECCI) of threading dislocations (TDs) normal to the surface in wurtzite group-III nitride materials. I also introduce and use the notion of ECC-strain to study crystal features and to predict the behaviour of TDs contrast.For the electron back(/forward)scatter modality, I show the first application of the new energy-weighted dynamical scattering capabilities of EMsoft to study the novel transmission mode (TKD) of the SEM.
Advisor / supervisor
  • Hourahine, Ben
  • Trager-Cowan, Carol
Resource Type
Note
  • Error on title page. Date reads 2019. Date of award is 2020.
DOI
Date Created
  • 2020
Former identifier
  • 9912792293002996

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