<p>Thin film materials have become increasingly complex in morphological
and structural design. When
characterizing the structure of these films, a crucial field of study is the
role that crystallite orientation plays in giving rise to unique electronic
properties. It is therefore important to
have a comparative tool for understanding differences in crystallite
orientation within a thin film, and also the ability to compare the structural
orientation between different thin films. Herein, we designed a new method dubbed the
mosaicity factor (MF) to quantify crystallite orientation in thin films using
grazing incidence wide-angle X-ray scattering (GIWAXS) patterns. This method for quantifying the orientation of
thin films overcomes many limitations inherent in previous approaches such as
noise sensitivity, the ability to compare orientation distributions along
different axes, and the ability to quantify multiple crystallite orientations
observed within the same Miller index. Following
the presentation of MF, we proceed to discussing case studies to show the
efficacy and range of application available for the use of MF. These studies show how using the MF approach
yields quantitative orientation information for various materials assembled on
a substrate.<b></b></p>
Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification
