Solving the spatial distributions of multicomponent polymer thin films with resonant soft x-ray scattering

The self-assembly of block copolymers into periodic nanostructures at length scales between 5-50 nm makes them ideal tools for nanofabrication. Systems with more than two components offer increased morphological flexibility over binary systems but are significantly more challenging to characterize due to poor contrast between components without additional labeling or staining. Resonant soft X-ray scattering (RSoXS) can provide tunable contrast based on the functional groups in the system, providing label-free access to rich, hyperspectral scattering data. However, interpretation of that data is complex and time intensive because it requires solving a non-trivial inverse problem. To address this issue, we have developed a contrast variation approach which deconvolutes the hyperspectral data into partial scattering functions (PSFs), analogous to neutron scattering methods. The resulting PSFs describe the spatial distribution of the individual components in the system that can then be compared against forward simulations, significantly reducing the difficulty of the analysis. We demonstrate the utility of this RSoXS technique through characterization of a model triblock copolymer thin film, poly(isoprene-block-styrene-2-vinyl pyridine), which exhibits rich morphological variations depending on its processing history. This contrast variation RSoXS technique is applicable to characterizing a broad range of multicomponent systems that are currently intractable through scattering or other conventional methods.