Optical proximity correction (OPC) models have been improving their accuracy over the years by modeling more error sources in lithographic systems, but model calibration techniques are improving at a slower pace. One area of modeling calibration that has garnered little interest is the statistical variance of the calibration data set.


This paper presents a feasibility study for treatment of data variance during model calibration. Its particular approach was developed to improve the model fitness for primary out-of-specification features present in the calibration test pattern by performing small manipulations of the measured data combined with data weighting during the model calibration process.

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