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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations
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Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

机译:空间方差谱分析及其在系统晶圆空间变化无监督检测中的应用

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Investigation of wafer spatial variations is critical for semiconductor process/equipment optimization and circuit design. The objective of spatial variation study is to differentiate the systematic variation component from the random component. This is usually done by contrasting with a set of known systematic patterns based on engineering knowledge. However, there could exist unknown systematic components remaining in the unexplained residuals and overlooked by the conventional spatial variation study. In this paper, we develop a novel spatial variance spectrum $(SVS)$ to analyze the systematic variations without any priori information of the systematic patterns. The $SVS$ is a series of spatial variations over a range of spatial moving window sizes from the smallest spatial moving window consisting of only two metrology sites to the largest one covering all metrology sites of the entire wafer. The $SVS$ can be used to characterize the wafer spatial variations and to detect existence of systematic variations by a proposed hypothesis test. We also propose an index to summarize from the $SVS$ the systematic proportion of the spatial variation. The proposed test and index of systematic variations will be demonstrated and validated through both hypothetical examples and actual cases of wafer critical dimension (CD) metrology data.
机译:晶圆空间变化的研究对于半导体工艺/设备优化和电路设计至关重要。空间变异研究的目的是区分系统变异成分和随机成分。通常,这是通过与基于工程知识的一组已知系统模式进行对比来完成的。但是,可能存在未知的系统成分残留在无法解释的残差中,并被常规的空间变异研究忽略了。在本文中,我们开发了一种新颖的空间方差谱$(SVS)$来分析系统变化,而无需任何先验信息的系统模式。 $ SVS $是一系列空间移动窗口尺寸上的一系列空间变化,从最小的仅包含两个度量站点的空间移动窗口到覆盖整个晶圆的所有度量站点的最大空间移动窗口。 $ SVS $可用于表征晶圆的空间变化并通过提出的假设检验来检测系统变化的存在。我们还提出了一个索引,从$ SVS $总结出空间变化的系统比例。拟议的测试和系统变化指数将通过假设示例和晶圆关键尺寸(CD)计量数据的实际案例进行演示和验证。

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