Adaptive OPC approach based on pattern grouping algorithm

C. M. Hu; C. T. Hsuan; H. Y. Hsieh; Fred Lo; Elvis Yang; T. H. Yang; K. C. Chen; Chih-Yuan Lu

doi:10.1117/12.2045371

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31 March 2014 Adaptive OPC approach based on pattern grouping algorithm

C. M. Hu, C. T. Hsuan, H. Y. Hsieh, Fred Lo, Elvis Yang, T. H. Yang, K. C. Chen, Chih-Yuan Lu

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Proceedings Volume 9052, Optical Microlithography XXVII; 90521X (2014) https://doi.org/10.1117/12.2045371
Event: SPIE Advanced Lithography, 2014, San Jose, California, United States

Abstract

OPC (Optical Proximity Correction) has been employed for over decade to address local lithographic printing effects. With the intensive scaling down of the designs as well as the increasing complexity of layout routing, lithographic process is being pushed to its theoretical limit and it has led to continuously shrinking DoF (Depth of Focus). Complex OPC model components are hence included into optical lithography simulation to ensure tolerable CD (Critical Dimension) variation and sustainable DOF of concerned layouts. For example, very complicated segmentation needs to be applied in mask correction, which comes at the cost of long runtime and requires an effective approach to consolidate the adequacy of model components during the flow of correction parameter tuning. In this paper, an approach is demonstrated to improve the accuracy and efficiency of OPC parameter tuning for mask correction. The approach starts with analyzing the target points in post-OPC database to identify those intolerable variations, followed by a pattern similarity grouping for the above intolerable layouts. Then, a concern index is established based on the CD out-of-tolerance ratio, dissection and pattern type for prioritizing the problematic variations. Then the corrective parameters are accordingly optimized to reduce the variation on highly prioritized patterns. During the iteration flow of OPC parameter optimization, the combination of pattern grouping and concern index greatly reduces required optimization iterations for OPC recipe tuning and enhances OPC convergence.

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C. M. Hu, C. T. Hsuan, H. Y. Hsieh, Fred Lo, Elvis Yang, T. H. Yang, K. C. Chen, and Chih-Yuan Lu "Adaptive OPC approach based on pattern grouping algorithm", Proc. SPIE 9052, Optical Microlithography XXVII, 90521X (31 March 2014); https://doi.org/10.1117/12.2045371

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