31 March 2014 Automated sample plan selection for OPC modeling
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Abstract
It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers’ insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.
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Nathalie Casati, Maria Gabrani, Ramya Viswanathan, Zikri Bayraktar, Om Jaiswal, David DeMaris, Amr Y. Abdo, James Oberschmidt, Andreas Krause, "Automated sample plan selection for OPC modeling", Proc. SPIE 9052, Optical Microlithography XXVII, 90520J (31 March 2014); doi: 10.1117/12.2045461; https://doi.org/10.1117/12.2045461
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