On uncertainties in the reconstruction of nanostructures in EUV scatterometry and grazing incidence small-angle X-ray scattering

Abstract

Increasing miniaturization and complexity of nanostructures require innovative metrology solutions with high throughput that can assess complex 3D structures in a non-destructive manner. EUV scatterometry is investigated for the characterization of nanostructured surfaces. The reconstruction is based on a rigorous simulation using a Maxwell solver based on finite-elements and is statistically validated with a Markov-Chain Monte Carlo sampling method. Here it is shown that this method is suitable for the dimensional characterization of the nanostructures and the investigation of oxide or contamination layers. In comparison to grazing-incidence small-angle X-rayscattering (GISAXS) EUV allows to probe smaller areas. The influence of the divergence on the diffracted intensities in EUV is much lower than in GISAXS, which also reduces the computational effort of the reconstruction.

References in corpus (9)

• emcee: The MCMC Hammer
• Non-Destructive, High-Resolution, Chemically Specific, 3D Nanostructure Characterization using Phase-Sensitive EUV Imaging Reflectometry
• Characterization of an in-vacuum PILATUS 1M detector
• Reconstructing Detailed Line Profiles of Lamellar Gratings from GISAXS Patterns with a Maxwell Solver
• Element sensitive reconstruction of nanostructured surfaces with finite elements and grazing incidence soft X-ray fluorescence
• Grazing Incidence Small Angle X-Ray Scattering (GISAXS) on Small Targets Using Large Beams
• Correlated Diffuse X-ray Scattering from Periodically Nano-Structured Surfaces
• Applicability of the Debye-Waller damping factor for the determination of the line-edge roughness of lamellar gratings
• Extracting Dimensional Parameters of Gratings Produced with Self-Aligned Multiple Patterning Using GISAXS

Cited by in corpus (4)

• Hybrid approach to reconstruct nanoscale grating dimensions using scattering and fluorescence with soft X-rays
• Mixed Noise and Posterior Estimation with Conditional DeepGEM
• Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions
• Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction