18 March 2016 Image-based pupil plane characterization via principal component analysis for EUVL tools
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Abstract
We present an approach to image-based pupil plane amplitude and phase characterization using models built with principal component analysis (PCA). PCA is a statistical technique to identify the directions of highest variation (principal components) in a high-dimensional dataset. A polynomial model is constructed between the principal components of through-focus intensity for the chosen binary mask targets and pupil amplitude or phase variation. This method separates model building and pupil characterization into two distinct steps, thus enabling rapid pupil characterization following data collection. The pupil plane variation of a zone-plate lens from the Semiconductor High-NA Actinic Reticle Review Project (SHARP) at Lawrence Berkeley National Laboratory will be examined using this method. Results will be compared to pupil plane characterization using a previously proposed methodology where inverse solutions are obtained through an iterative process involving least-squares regression.
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Zac Levinson, Zac Levinson, Andrew Burbine, Andrew Burbine, Erik Verduijn, Erik Verduijn, Obert Wood, Obert Wood, Pawitter Mangat, Pawitter Mangat, Kenneth A. Goldberg, Kenneth A. Goldberg, Markus P. Benk, Markus P. Benk, Antoine Wojdyla, Antoine Wojdyla, Bruce W. Smith, Bruce W. Smith, "Image-based pupil plane characterization via principal component analysis for EUVL tools", Proc. SPIE 9776, Extreme Ultraviolet (EUV) Lithography VII, 977618 (18 March 2016); doi: 10.1117/12.2219745; https://doi.org/10.1117/12.2219745
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