使用GaAs/AlAs多层认证参考物质的厚度和密度确定X射线反射法中的样品对准
Determining sample alignment in X-ray Reflectometry using thickness and density from GaAs/AlAs multilayer certified reference materials
摘要：
X射线反射仪（XRR）为研究人员和制造商提供了一种非破坏性的方法来确定沉积在光滑基底上的薄膜的厚度、粗糙度和密度。由于对这种现象建模的方程的嵌套性质，仪器对准和参数估计精度之间的相互关系有点不透明。在这项研究中，我们有意转移包含在高质量的XRR数据集中的入射角信息，并使用相同的结构模型来评估角度不对准对参数估计的影响，从而改进这种转移的数据。我们开发了一系列的校准曲线与层的厚度和密度的多层GaAs/AlAs标准参考材料的GaAs衬底上的变化与数据不对准。然后，我们测试的有效性和鲁棒性的几种方法，使用已知的厚度和密度参数从这个结构来校准仪器对准。我们发现最高的灵敏度和线性度与埋层的AlAs和GaAs层的测量失调，相反，表面层，这表明最不稳定。这是一个偶然的结果，因为掩埋的AlAs和GaAs在厚度上表现出最高的长期稳定性。因此，确实可以使用参考厚度估计来验证XRR角度对准精度。埋层质量密度信息也显示出作为一种稳健的校准方法的前景。这是令人惊讶的，因为埋层的电子密度既是一种高度相关的现象，也是XRR模型中的一个微妙组成部分。
英文摘要：
X-ray reflectometry (XRR) provides researchers and manufacturers with a non-destructive way to determine thickness, roughness, and density of thin films deposited on smooth substrates. Due to the nested nature of equations modeling this phenomenon, the inter-relation between instrument alignment and parameter estimation accuracy is somewhat opaque. In this study, we intentionally shift incident angle information contained in a high-quality XRR data set and refine this shifted data using an identical structural model to assess the effect angle misalignment has on parameter estimation. We develop a series of calibration curves relating data misalignment to variation with layer thickness and density for a multilayer GaAs/AlAs Certified Reference Material on a GaAs substrate. We then test the validity and robustness of several approaches of using known thickness and density parameters from this structure to calibrate instrument alignment. We find the highest sensitivity to and linearity with measurement misalignment from buried AlAs and GaAs layers, in contrast to the surface layers, which show the most instability. This is a fortuitous result, as buried AlAs and GaAs exhibit the highest long-term stability in thickness. Therefore, it is indeed possible to use reference thickness estimates to validate XRR angle alignment accuracy. Buried layer mass density information also shows promise as a robust calibration approach. This is suprising, as electron density of buried layers is both a highly-correlated phenomenon, and a subtle component in the XRR model.
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