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13 October 2011 Simulation based mask defect printability verification and disposition, part II
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We have reported the first part of the work in 2009 BACUS meeting [1], using primarily SEM mask defect images as input. This paper is the extension of that work using mask optical inspection images with a new image process algorithm. Simulation has been widely used in overall lithography process, called computational lithography, as an effective way for cost and time reduction. As the industry moves towards 45nm and 32nm technology nodes in production, the mask inspection, with increased sensitivity and shrinking critical defect size, catches more and more nuisance and false defects. Increased defect counts pose great challenges in the post inspection defect classification and disposition: which defects are real defects, and among the real defects, which defects should be repaired and how to verify the post-repair defects. In this paper, we report simulation mask defect printability check and disposition results extending beyond SEM mask defect images [1] into optical inspection mask defects images to demonstrate cost and time reduction by simulation in mask defect management area. A new algorithm has been developed in the software tool to convert optical inspection mask defect images into "pseudo-defect" polygons in GDS format. Then, the converted defect polygons were filled with the correct tone to form mask patterns and were merged back into the original design GDS. With lithography process model, the resist contour of area of interest (AOI-the area surrounding a mask defect) can be simulated. If such complicated model is not available, a simple optical model can be used to get aerial image intensity of AOI. With build-in contour analysis functions, the software can easily compare the contour (or intensity) differences between real mask (with defect) and normal mask (without defect). With user provided judging criteria, software can be easily disposition the defect based on contour comparison. The software has been tested and adapted for production use. We will present some accuracy test results against AIMS tool or wafer CDs in defect printability check.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric Guo, Irene Shi, Blade Gao, Nancy Fan, Guojie Cheng, Li Ling, Ke Zhou, Gary Zhang, Ye Chen, Chingyun Hsiang, and Bo Su "Simulation based mask defect printability verification and disposition, part II", Proc. SPIE 8166, Photomask Technology 2011, 81662D (13 October 2011);

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