10 March 2006 Multivariate visualization techniques in statistical process monitoring and their applications to semiconductor manufacturing
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Proceedings Volume 6155, Data Analysis and Modeling for Process Control III; 615506 (2006); doi: 10.1117/12.654945
Event: SPIE 31st International Symposium on Advanced Lithography, 2006, San Jose, California, United States
Abstract
In today's semiconductor industry, massive amount of data are easily made available in sensor equipped and computer controlled processes, but at the same time, the visualization of high dimensional data has been difficult. This work gives an overview of the most commonly used multivariate visualization techniques for the visualization of high dimensional data. The visualization of process dynamics in the original variable space is discussed. This work also presents visualization of data clusters in the transformed space using projection methods, such as principal component analysis (PCA), class preserving projection (CPP) and methods proposed in this work based on Fisher discriminant analysis (FDA), and support vector machines (SVM), as well as the discussion of the visualization of the process dynamics in the transformed low dimensional space. A rapid thermal annealing process data set is used through the paper as the example data set for comparison of various visualization techniques.
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Q. Peter He, "Multivariate visualization techniques in statistical process monitoring and their applications to semiconductor manufacturing", Proc. SPIE 6155, Data Analysis and Modeling for Process Control III, 615506 (10 March 2006); doi: 10.1117/12.654945; https://doi.org/10.1117/12.654945
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KEYWORDS
Visualization

Principal component analysis

Data modeling

Visual analytics

Process control

Dynamical systems

Semiconductors

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