Paper
18 March 2016 An automated image-based tool for pupil plane characterization of EUVL tools
Author Affiliations +
Abstract
Pupil plane characterization will play a critical role in image process optimization for EUV lithography (EUVL), as it has for several lithography generations. In EUVL systems there is additional importance placed on understanding the ways that thermally-induced system drift affect pupil variation during operation. In-situ full pupil characterization is therefore essential for these tools. To this end we have developed Quick Inverse Pupil (QUIP)—a software suite developed for rapid characterization of pupil plane behavior based on images formed by that system. The software consists of three main components: 1) an image viewer, 2) the model builder, and 3) the wavefront analyzer. The image viewer analyzes CDSEM micrographs or actinic mask micrographs to measure either CDs or through-focus intensity volumes. The software is capable of rotation correction and image registration with subpixel accuracy. The second component pre-builds a model for a particular imaging system to enable rapid pupil characterization. Finally, the third component analyzes the results from the image viewer and uses the optional pre-built model for inverse solutions of pupil plane behavior. Both pupil amplitude and phase variation can be extracted using this software. Inverse solutions are obtained through a model based algorithm which is built on top of commercial rigorous full-vector simulation software.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zac Levinson, Jack S. Smith, Germain Fenger, and Bruce W. Smith "An automated image-based tool for pupil plane characterization of EUVL tools", Proc. SPIE 9776, Extreme Ultraviolet (EUV) Lithography VII, 97762M (18 March 2016); https://doi.org/10.1117/12.2219704
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KEYWORDS
Extreme ultraviolet lithography

Image filtering

Wavefronts

Monochromatic aberrations

Image processing

Principal component analysis

Data modeling

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