The new test pattern selection method for OPC model calibration, based on the process of clustering in a hybrid space

Abstract

Model-based Optical Proximity Correction (OPC) is widely used in advanced lithography processes. The OPC model contains an empirical part, which is calibrated by fitting the model with data from test patterns. Therefore, the success of the OPC model strongly relies on a test pattern sampling method. This paper presents a new automatic sampling method for OPC model calibration, using centroid-based clustering in a hybrid space: the direct sum of geometrical sensitivity space and image parameter space. This approach is applied to an example system in order to investigate the minimum size of a sampling set, so that the resulting calibrated model has the error comparable to that of the model built with a larger sampling set. The proposed sampling algorithm is verified for the case of a contact layer of the most recent logic device. Particularly, test patterns with both 1D and 2D geometries are automatically sampled from the layer and then measured at the wafer level. The subsequent model built using this set of test patterns provides high prediction accuracy.