Contour-based model calibration to a minimum number of patterns

Computational lithography applications for OPC/ RET utilize models that represent the lithographic process in simulations. The quality of OPC/ RET wafer results strongly depends on the quality of the model. Hence, achieving model quality and experimental match is the goal of the model calibration process where models are calibrated to experimental data. Ideally, the model would be calibrated and validated to a data set that completely covers the entire design space and all process conditions. A promising alternative to the traditionally applied SEM-CD-based model calibration is the calibration to pattern contours directly with benefits in design space coverage, reduced metrology effort and data preparation complexity. However, contour calibration also demands a new standard operating procedure for contour specific metrology, pattern design and calibration. Goal of this work is to develop and exercise a full contour-based calibration methodology. Firstly, we discuss preconditions for a successful calibration: good quality contour input data, predictive modeling of optics, mask topography and 3D resist and additional calibrator functionality to include aspects of alignment and pattern specific measurement confidence. Secondly, we assess pattern for their calibration-suitability using a metric for pattern information density. Experiments are performed to show the applicability of the metric and the potential to calibrate to a minimal set of patterns. A model calibrated to a well selected single 2.25 μm² contour is predicting a large set of pattern contours, 3D resist characteristics and SEM-CD focus-exposure process windows.