In microelectronics and semiconductor devices, thin films are of utmost importance in terms of reliability. In order to design such reliable thin films, residual stresses determination and microstructural characterization plays a crucial role. Here, various advanced techniques like FE-EBSD, FESEM-TKD (Transmission Kikuchi diffraction) and high resolution angular EBSD (HR-EBSD) are carried out to perform microstructural characterization qualitatively as well as quantitatively. Particularly, HR-EBSD provides novel insights with respect to the strain and stress distribution on the thin film surface as well as on the cross section. In addition to the experiment, we have designed a novel artificial intelligence-based model to analyze the Kikuchi patterns and provide a detailed statistical analysis for the microstructure. In particular, we gain knowledge about the evolution of residual stresses, rotational strains and slip system movements in the grain and along the grain boundaries. We correlate the HR-EBSD results with measurements of the residual stress profile performed along the thickness of the thin films. For the latter, we use the ion layer removal method with a machine learning prediction model. The presented workflow shall help to design improved thin films for future industrial applications.