Kernel Count Reduction in Model Based Optical Proximity Correction Process Models

Abstract

As the leading-edge photolithography is approaching rapidly closer to the theoretical optical resolution limit, model based optical proximity correction (MBOPC) has evolved from a nice-to-have feature to a must-have feature. The purpose of MBOPC is to adjust the designed pattern on the photomask to introduce mask perturbations such that the layout printed on the wafer is as close as possible to the drawn layout. Before MBOPC is performed across a full chip, a process model is calibrated based upon manufacturing process data measured from scanning electron microscope (SEM) pictures of test patterns. The process model is usually composed of two components, signal intensity and threshold, and the intersections of these two components determine the layout contours that will be printed on wafer. It is found that the accuracy of the process model is improved as more kernels are used to represent the model. However, pattern correction runtime is proportional to the number of kernels used for the model, thereby introducing a constraint to incorporate least number of kernels in the process model. In this study, a novel method of computing the signal change in the MBOPC process is proposed, which is used to maintain the correction accuracy and reduce the MBOPC runtime.