Paper
13 March 2012 Pattern selection in high-dimensional parameter spaces
Georg Viehoever, Brian Ward, Hans-Juergen Stock
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Abstract
Pattern selection for OPC model calibration is frequently done by image parameter space (IPS) coverage methods. These ensure that the images of the chosen test patterns cover important regions of an n-dimensional parameter space spawned by image parameters, such as minimum and maximum intensity I_min, I_max, curvature, slope and image density. But such a small number of parameters is often insufficient for finding a good set of patterns for the calibration process. We present results for the extended nPS method which ensures coverage of a high dimensional parameter space with a high number of parameters, even permitting the use of all pixels of the aerial images (n >> 1000) as parameters.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Georg Viehoever, Brian Ward, and Hans-Juergen Stock "Pattern selection in high-dimensional parameter spaces", Proc. SPIE 8326, Optical Microlithography XXV, 832618 (13 March 2012); https://doi.org/10.1117/12.916352
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Cited by 9 scholarly publications and 1 patent.
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KEYWORDS
Calibration

Current controlled current source

Optical lithography

Optical proximity correction

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