Generation of 3D random topography datasets with periodic boundaries for surface metrology algorithms and measurement standards
Generation of 3D random topography datasets with periodic boundaries for surface metrology algorithms and measurement standards
复制标题
DOI：
10.1016/j.wear.2010.04.035
复制
发表时间：
2011-06-03
期刊：
影响因子：
5
通讯作者：
Iwabuchi, A.
中科院分区：
文献类型：
--
作者：
Uchidate, M.;Yanagi, K.;Iwabuchi, A.
提出了一种生成具有周期性边界的三维（3D）随机地形数据集的方法，用于评估表面计量算法和测量标准的原始数据。一个非因果的二维（2D）自回归（AR）模型，它表示的表面作为一个线性加权总和的AR参数和地形数据，除了随机噪声分量，被施加到计算生成3D随机地形数据。通过使用假定周期性边界的扩展，生成的数据的边缘在边界上变得连续。已经证实，光谱特性不受此扩展的影响。这种技术提供了表面计量，如过滤和光谱分析的计算技术的评估的优势，因为边缘效应可以避免通过假设周期性的边界，和固有的技术效果可以进行评估。此外，为了用作用于仪器校准的随机测量标准，可以类似于地板砖在测量窗口中简单地重复布置所生成的数据，而不会在数据的边界处引入不连续的边缘。(C)2010 Elsevier B.V.保留所有权利。
A procedure is presented to generate three-dimensional (3D) random topography datasets with periodic boundaries for the evaluation of surface metrology algorithms and original data for measurement standards. A non-causal two-dimensional (2D) autoregressive (AR) model, which expresses the surface as a linear weighted summation of AR parameters and topography data in addition to a random noise component, is applied to computationally generate 3D random topography data. By the use of an extension that assumes periodic boundaries, the edges of the generated data become continuous across the boundaries. It has been verified that the spectral properties are not affected by this extension. This technique offers advantages for the evaluation of computational techniques for surface metrology, such as filtrations and spectral analysis since the edge effect can be avoided by assuming periodic boundaries, and inherent effects of the techniques can be evaluated. In addition, for use as a random measurement standard for instrument calibration, it is possible to simply arrange the generated data repeatedly in the measuring window similarly to floor tiles without introducing discontinuous edges at the boundaries of the data. (C) 2010 Elsevier B.V. All rights reserved.