The process model is a major factor affecting the quality of the Model Based Optical Proximity Correction (OPC). A better process model directly leads to better OPC, hence better yield and more profit. The traditional way in calibrating these process models is using CD measurements at sample locations in the test chip. However, the use of Scanning Electron Microscope (SEM) image contours for process model calibration and optimization has been recently introduced in an attempt to build more predictable models. In this study, we characterize the traditional flow models versus the contour calibrated models and study the effect of using different combinations and weighting schemes on the quality of the resulting process models, its stability and its ability to correctly predict the process.

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