Improving model-based optical proximity correction accuracy using improved process data generation

Mark Lu; Dion King; Curtis Liang; Lawrence S. Melvin III

doi:10.1117/12.674217

9 June 2006 Improving model-based optical proximity correction accuracy using improved process data generation

Mark Lu, Dion King, Curtis Liang, Lawrence S. Melvin III

Proceedings Volume 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies; 61490U (2006) https://doi.org/10.1117/12.674217
Event: 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies, 2005, Xian, China

Abstract

Model-based Optical Proximity Correction (MBOPC) is used to make systematic modifications to transfer a pattern's design intent from a drawn database to a wafer. This is accomplished by manipulating the shape of mask features to generate the desired pattern (design intent) on the wafer. MBOPC accomplishes this task by dividing drawn patterns into segments, then using a process model to manipulate these segments to achieve the design intent on the wafer. The generation of an accurate process model is very important to the MBOPC process because it contains the process information used to manipulate correction segments. When corrected data are written on a reticle, the faithful and well-controlled reproduction of the data on the mask is critical to realizing the desired lithographic performance. This paper will explore methodologies to improve model accuracy using mask fabrication data and process test patterns. Model accuracy improvement will be accomplished using intelligent sampling plans and representative mask structures. The sampling plan needs to identify critical device and process features. The test mask used to generate the process model needs to have test structures to gather process data. The test mask also must have test structures that can evaluate model quality by testing the extrapolation and interpolation of the model to data that was no used to generate the process model. These methodologies will be shown to improve final mask pattern quality.

Citation Download Citation

Mark Lu, Dion King, Curtis Liang, and Lawrence S. Melvin III "Improving model-based optical proximity correction accuracy using improved process data generation", Proc. SPIE 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 61490U (9 June 2006); https://doi.org/10.1117/12.674217

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