Multislice forward modeling of Coherent Surface Scattering Imaging on surface and interfacial structures

TL;DR

This paper introduces a multislice forward model for Coherent Surface Scattering Imaging (CSSI) that accurately simulates dynamical scattering phenomena and enables 3D surface structure reconstruction from single-shot images using GPU-accelerated optimization.

Contribution

The authors develop a novel multislice forward model for CSSI that overcomes limitations of traditional Fourier-based models and facilitates 3D reconstruction from single images.

Findings

The multislice model accurately reproduces dynamical scattering near the critical angle.

The model enables 3D pattern reconstruction from a single CSSI image.

GPU-accelerated optimization achieves fast reconstruction performance.

Abstract

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle x-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a highly coherent beam is used. Coherent Surface Scattering Imaging (CSSI) is a powerful yet non-invasive technique similar to Coherent X-ray Diffractive Imaging (CDI) but performed at small angles and grazing-incidence reflection geometry. A challenge with CSSI is that conventional CDI reconstruction techniques cannot be directly applied to CSSI because the Fourier-transform-based forward models cannot reproduce the dynamical scattering phenomenon near the critical angle of total external reflection of the substrate-supported samples. To overcome this challenge, we have developed a multislice forward model which can successfully simulate the dynamical or multi-beam scattering generated from surface structures and the underlying substrate. The forward model is also demonstrated to be able to reconstruct an elongated 3D pattern from a single shot scattering image in the CSSI geometry through fast-performing CUDA-assisted PyTorch optimization with automatic differentiation.