Mareček David, Oberreiter Julian, Nelson Andrew, Kowarik Stefan
Physikalische und Theoretische Chemie, Universität Graz, Heinrichstraße 28, Graz, 8010, Austria.
ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia.
J Appl Crystallogr. 2022 Oct 1;55(Pt 5):1305-1313. doi: 10.1107/S2053273322008051.
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. The approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with less beam damage.
本文提出了一种分析实时X射线反射率（XRR）过程数据的方法，该方法不仅像通常那样将数据作为倒易空间矢量大小的函数进行分析，而且作为其大小和时间两者的函数进行分析。从XRR曲线中提取的实空间结构被限制为物理信息生长模型的解，并使用最新的卷积神经网络（CNN）和差分进化拟合来共同优化薄膜生长实验的多个时间相关XRR曲线（，）。因此，即使XRR数据点被稀疏采样、减少了七倍，或者由于计数时间减少了200倍而导致数据有噪声，也能够以与单个XRR曲线的标准拟合相当的保真度正确分析XRR数据。使用CNN分析并通过动力学模型纳入先验信息的方法不仅限于生长研究，还可以轻松扩展到其他动力学X射线或中子反射率数据，以实现更快的测量且减少束损伤。
