Fully commercial exploitation of graphene properties through its integration
in the Si-dominated electronic technologies needs to reach challenging
growth and processing standards. Indeed, fabrication and subsequent
processing of graphene using current methodologies, such as chemical vapor deposition oriented to industrial fabrication, require discriminating and assessing strain and corrugation in order to achieve practical use. For the first time, a combination of ab initio simulations and experimental hyperspectral near-edge X-ray absorption fine structure (NEXAFS) data was successfully employed to monitor mechanical deformation in graphene (i.e., strain and corrugation) at wafer-scale dimensions. Notably, this innovative assessment offers promise towards wafer-scale characterization to inform industrial manufacturing. To this end, the first study presented in this thesis examines the atomic sensitivity to corrugation of the dichroic ratio, orbital vector approximation, and unsupervised machine learning methods. Significantly, the orbital vector approach proved to be the method with the highest sensitivity as
reflected by the measured parameter, which also strongly depends on
the frequency of the defects. In the second study of this thesis, a new
methodology to assess strain with the aid of theoretical samples taken
as standards is presented. The most significant results are its accurate
strain estimation of chemical vapor deposition-grown graphene on Cu
substrates and reasonably accurate estimation of transferred multilayers
of graphene onto SiC substrates. In the last study, a method to simulate
characteristic angle-resolved NEXAFS spectra (spectral fingerprints) of
representative topological defects on graphene is presented and proves
to effectively analyze the correlation between the spatial extent of the
defect and its spectral fingerprints. This theoretical database of point
defects can contribute to the analysis and interpretation of complex experimental
spectroscopic data.