Quantitative methods for electron energy loss spectroscopy

• Autores: Alberto Eljarrat Ascunce
• Directores de la Tesis: Francisca Peiró Martínez (dir. tes.), Sonia Estradé Albiol (dir. tes.)
• Lectura: En la Universitat de Barcelona ( España ) en 2015
• Idioma: inglés
• Tribunal Calificador de la Tesis: Helmut Kohl (presid.), Francesc Salvat (secret.), Ana Maria Sanchez Fuentes (voc.)
• Materias:
  • Física
    • Electrónica
      • Microscopia electrónica
    • Física del estado sólido
      • Semiconductores
• Enlaces
  • Tesis en acceso abierto en:  TDX  Dipòsit Digital de la UB 
• Resumen

  • This thesis explores the analytical capabilities of low-loss electron energy loss spectroscopy (EELS), applied to disentangle the intimate configuration of advanced semiconductor heterostructures. Modern aberration corrected scanning transmission electron microscopy (STEM) allows extracting spectroscopic information from extremely constrained areas, down to atomic resolution. Because of this, EELS is becoming increasingly popular for the examination of novel semiconductor devices, as the characteristic size of their constituent structures shrinks. Energy-loss spectra contain a high amount of information, and since the electron beam undergoes well-known inelastic scattering processes, we can trace the features in these spectra down to elementary excitations in the atomic electronic configuration. In Chapter 1, the general theoretical framework for low-loss EELS is described. This formulation, the dielectric model of inelastic scattering, takes into account the electrodynamic properties of the fast electron beam and the quantum mechanical description of the materials. Low-loss EELS features are originated both from collective mode (plasmons) and single electron excitations (e.g. band gap), that contain relevant chemical and structural information. The nature of these excitations and the inelastic processes involved has to be taken into account in order to analyze experimental data or to perform simulations. The computational tools required to perform these tasks are presented in Chapter 2. Among them, calibration, deconvolution and Kramers-Kronig analysis (KKA) of the spectrum constitute the most relevant procedures, that ultimately help obtain the dielectric information in the form of a complex dielectric function (CDF). This information may be then compared to the one obtained by optical techniques or with the results from simulations. Additional techniques are explained, focusing first on multivariate analysis (MVA) algorithms that exploit the hyperspectral acquisition of EELS, i.e. spectrum imaging (SI) modes. Finally, an introduction to the density functional theory (DFT) simulations of the energy-loss spectrum is given. In Chapter 3, DFT simulations concerning (Al, Ga, In)N binary and ternary compounds are introduced. The prediction of properties observed in low-loss EELS of these semiconductor materials, such as the band gap energy, is improved in these calculations. Moreover, a super-cell approach allows to obtain the composition dependence of both band gap and plasmon energies from the theoretical dielectric response coefficients of ternary alloys. These results are exploited in the two following chapters, in which we experimentally probe structures based on group-III nitride binary and ternary compounds. In Chapter 4, two distributed Bragg reflector structures are examined (based upon AlN/GaN and InAlN/GaN multilayers, respectively) through different strategies for the characterization of composition from plasmon energy shift. Moreover; HAADF image simulation is used to corroborate he obtained results; plasmon width, band gap energy and other features are measured; and, KKA is performed to obtain the CDF of GaN. In Chapter 5, a multiple InGaN quantum well (QW) structure is examined. In these QWs (indium rich layers of a few nanometers in width), we carry out an analysis of the energy-loss spectrum taking into account delocalization and quantum confinement effects. We propose useful alternatives complementary to the study of plasmon energy, using KKA of the spectrum. Chapters 6 and 7 deal with the analysis of structures that present pure silicon-nanocrystals (Si-NCs) embedded in silicon-based dielectric matrices. Our aim is to study the properties of these nanoparticles individually, but the measured low-loss spectrum always contains mixed signatures from the embedding matrix as well. In this scenario, Chapter 6 proposes the most straightforward solution; using a model-based fit that contains two peaks. Using this strategy, the Si-NCs embedded in an Er-doped SiO2 layer are characterized. Another strategy, presented in Chapter 7, uses computer-vision tools and MVA algorithms in low-loss EELS-SIs to separate the signature spectra of the Si-NCs. The advantages and drawbacks of this technique are revealed through its application to three different matrices (SiO2, Si3N4 and SiC). Moreover, the application of KKA to the MVA results is demonstrated, which allows to extract CDFs for the Si-NCs and surrounding matrices.