Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial...

Area of Science:

• Materials Science
• Physics
• Data Science

Background:

• Real-time X-ray reflectivity (XRR) is crucial for monitoring thin film growth.
• Traditional XRR analysis focuses on reciprocal-space vector magnitude (q), limiting dynamic insights.
• Challenges include data sparsity and noise, often requiring extensive measurement times.

Purpose of the Study:

• To develop a novel approach for analyzing time-dependent XRR data R(q, t).
• To enhance the fidelity and efficiency of XRR data analysis for thin film growth experiments.
• To enable faster measurements with reduced data requirements and improved noise handling.

Main Methods:

• Analysis of XRR data as a function of both reciprocal-space vector (q) and time (t).
• Integration of a physics-informed growth model to constrain real-space structure solutions.
• Application of state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting for co-refining multiple XRR curves.

Main Results:

• Achieved analysis fidelity comparable to standard fits of individual XRR curves.
• Demonstrated successful analysis with a sevenfold reduction in data points for sparsely sampled data.
• Showcased robust performance with a 200-fold reduction in counting times for noisy data.

Conclusions:

• The CNN-based approach with kinetic modeling significantly improves XRR data analysis efficiency and accuracy.
• This method is adaptable to various kinetic X-ray and neutron reflectivity studies.
• Facilitates faster experimental measurements with reduced beam damage, advancing materials characterization.