Versatile 3D reconstruction framework for hard X-ray grazing incidence imaging of nanostructures
Luke Besley, P. S. Jørgensen, A. Diaz, C. Detlefs, S. De Angelis, M. Carlsen, B. Chang, C. Silvestre, J. W. Andreasen
TL;DR
The paper tackles the challenge of 3D nanoscale reconstruction from grazing-incidence coherent imaging, where traditional DWBA-based projection methods fail to capture strong multiple scattering. It introduces a multislice forward model for ptychography that propagates the wavefield through perpendicular slices, enabling depth-resolved phase retrieval across arbitrary incidence and rotation geometries. The approach is implemented in the open-source PyGRAPES package and validated on experimental GISAXS data and diverse simulations, including rotated samples, nanoparticle growth, and multilayer stacks. The results demonstrate high lateral resolution and meaningful depth information for surface and near-surface structures, with broad implications for detailed surface characterization and materials science.
Abstract
Coherent imaging techniques such as ptychography offer powerful capabilities for 3D resolution of nanoscale structures. By application in grazing incidence, such techniques may achieve exceptional surface sensitivity as demonstrated by grazing incidence small angle scattering. This requires however an extension of the conventional analysis based on the Distorted Wave Born Approximation which is typically limited to stratified-layer models and statistical descriptions of in-plane structures. The prevailing implementations of reconstruction algorithms for ptychography based on the projection approximation fails to capture the significant multiple scattering that occurs in grazing incidence. We present a ptychographic reconstruction framework that replaces the single-scattering model with a multislice wave-propagation formalism tailored to grazing incidence. The framework supports simultaneous phase retrieval and reconstruction, and can incorporate multiple incidence angles, multiple rotation angles, and flexible experimental geometries into a single inversion. Reconstructions can be initialized from a random guess without strong structural priors, enabling the recovery of complex surface and near-surface nanostructures. This reconstruction framework is applied to both experimental and simulated datasets, demonstrating its versatility.
