AI Article Synopsis

  • This work introduces an open-source software package that simulates X-ray reflectivity (XRR) and helps reconstruct film structures from measured XRR curves.
  • The software features a user-friendly interface for interactive simulation and uses a recursive method based on Fresnel equations, with tools for modeling multilayer structures.
  • The latest version includes enhancements like automatic fitting for XRR curves using an improved optimization algorithm and a new single-window interface for a better user experience.

Article Abstract

This work introduces (), an open-source software package designed to simulate X-ray reflectivity (XRR) and address the inverse problem of reconstructing film structures on the basis of measured XRR curves. features a user-friendly graphical interface that facilitates interactive simulation and reconstruction. The software employs a recursive approach based on the Fresnel equations to calculate XRR and incorporates specialized tools for modeling periodic multilayer structures. This article presents the latest version of the software (), with notable improvements. These enhancements encompass an automatic fitting capability for XRR curves utilizing a modified flight particle swarm optimization algorithm. A novel cost function was also developed specifically for fitting XRR curves of periodic structures. Furthermore, the overall user experience has been enhanced by developing a new single-window interface.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11001402PMC
http://dx.doi.org/10.1107/S1600576724001031DOI Listing

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Article Synopsis
  • This work introduces an open-source software package that simulates X-ray reflectivity (XRR) and helps reconstruct film structures from measured XRR curves.
  • The software features a user-friendly interface for interactive simulation and uses a recursive method based on Fresnel equations, with tools for modeling multilayer structures.
  • The latest version includes enhancements like automatic fitting for XRR curves using an improved optimization algorithm and a new single-window interface for a better user experience.
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X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work.

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Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described.

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Formation of a pure Langmuir monolayer of lysozyme at the air-water interface and its investigation by means of a surface pressure ()-mean molecular area () isotherm has been accomplished under different subphase pH conditions. A normalized area-time curve confirms the stable nature of the lysozyme monolayer whose compressibility variation with an increased surface pressure at specific subphase pH has also been studied from - isotherms. The monolayers exhibit irreversible hysteresis behaviour irrespective of subphase pH conditions, as evidenced from successive compression-expansion - isotherm cycles.

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An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector , as is commonly done, but as a function of both and time. The real-space structures extracted from the XRR curves are restricted to be solutions of a physics-informed growth model and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves (, ) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves, even if they are sparsely sampled, with a sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times.

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