Shape- and Element-Sensitive Reconstruction of Periodic Nanostructures with Grazing Incidence X-ray Fluorescence...

Area of Science:

• Materials Science
• Nanotechnology
• Surface Science

Background:

• Sub-nanometer characterization is vital for advanced integrated electronic circuits and transistor architectures like FinFETs.
• Current techniques require precise dimensional and compositional analysis of complex nanostructures.
• X-ray standing wave fields offer potential for nanoscale sensing but require advanced analysis.

Purpose of the Study:

• To develop and validate a novel, element-sensitive, non-destructive characterization technique for nanostructured surfaces.
• To utilize X-ray fluorescence (XRF) in grazing incidence geometry combined with machine learning for efficient analysis.
• To achieve sub-nanometer resolution for dimensional and compositional profiling of nanostructures.

Main Methods:

• Employing X-ray standing wave fields generated by X-ray fluorescence in grazing incidence geometry.
• Utilizing Bayesian optimization, a machine learning algorithm, to accelerate computational analysis and estimate parameter uncertainties.
• Extracting unknown optical constants via soft X-ray reflectometry and comparing results with scanning electron microscopy (SEM) and atomic force microscopy (AFM).

Main Results:

• Demonstrated sub-nanometer resolution for characterizing nanostructures made of silicon nitride and silicon oxide.
• Successfully reconstructed spatial element distribution profiles using finite element methods and XRF data.
• Validated the accuracy of the XRF method by comparing with SEM and AFM, and assessed the impact of surface contamination.

Conclusions:

• The novel XRF technique provides highly sensitive, non-destructive characterization of nanostructures with sub-nanometer precision.
• Machine learning integration significantly reduces computational time, making the method practical for real-time applications.
• This approach is crucial for the development and quality control of next-generation electronic devices.