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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. [ABSTRACT FROM AUTHOR]

In modern photolithography, as the feature size becomes smaller and smaller, it becomes more and more popular to include sub-resolution assist features (SRAFs) to improve the robustness of the lithography process. Hence, it is vital to simulate the process precisely with SRAFs placement. However, for computational reasons, it is necessary to model features with SRAFs placed using specially developed approximation algorithms. This need arises because of the inherent differences between SRAFs and main features. First, SRAFs are usually hard to verify physically because of their small sizes and, therefore, there is relatively large mask measurement uncertainty on the layout of SRAFs compared to main features. Second, unlike the well-defined main features, the shape of SRAFs is relatively poor, e.g., line edge roughness and critical dimension (CD) variation and, hence, the effective transmission of SRAFs may not be the same as for the main features. Third, the thickness of the mask is comparable to the feature sizes of SRAFs and the three-dimensional (3D) mask effect may play an important role in process simulation. In wafer measurements, the data are usually influenced by all the above effects, and it is practically impossible to identify them individually. In this study we propose a lumped treatment specially designed for SRAFs. The results show significant improvement in the simulation accuracy and reduce the RMS of fitting errors down to the sub-nanometer level for all features with or without placed SRAFs. [ABSTRACT FROM AUTHOR]