Scanning tunnelling microscopy of magnetic van der Waals materials

  • Olivia Rachel Armitage

Student thesis: Doctoral Thesis (PhD)


Understanding the properties of materials is essential for control of their behaviour for future applications. Devices based on two-dimensional van der Waals materials have wide-ranging technological possibilities, from computer components to light-emitting diodes and sensors. Over the last twenty years, these potential applications have begun to be explored experimentally, and a variety of materials have been studied in monolayer form, including semiconductors, superconductors and, more recently, magnetic materials. These are necessary for realising spintronic devices, which would offer higher performance and lower energy loss than existing charge-based technologies. However, two-dimensional materials are currently far less well understood than silicon, the principal material in modern electronic devices. For a complete understanding of their behaviour, investigation using a range of experimental and theoretical techniques is necessary.

In this thesis I present the setup of an ultra-high vacuum scanning tunnelling microscope (STM), compatible with molecular beam epitaxy and angle-resolved photoemission spectroscopy facilities, and capable of measuring the structural and electronic properties of monolayer samples at low temperature. This STM is used to study thin film samples of the magnetic materials chromium selenide and chromium telluride. The results are compared with theoretical calculations of the electronic structure and simulated scanning tunnelling microscopy (STM) images. I also present STM measurements of bulk Fe₃GeTe₂, investigating its electronic and magnetic properties. For these materials I show how experimental and theoretical methods can be combined to determine crystal structures, interpret electron scattering patterns and investigate the effects of magnetic order and electron correlations on the band structure. I also explore applications of machine learning in the analysis of STM images, using convolutional neural networks to detect features on the surfaces of PdCrO₂ and sulphur-doped FeSe. The demonstration of the application of these techniques to study the properties of two-dimensional materials provides the methods for future investigations.
Date of Award10 Jun 2024
Original languageEnglish
Awarding Institution
  • University of St Andrews
SupervisorPeter Wahl (Supervisor) & Phil King (Supervisor)


  • Scanning tunnelling microscopy
  • Magnetism
  • Two-dimensional materials

Access Status

  • Full text embargoed until
  • 24 April 2026

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