Poster + Paper
22 April 2024 Advanced pattern selection and coverage check for computational lithography
Author Affiliations +
Conference Poster
Abstract
Pattern selection for OPC (Optical Proximity Correction) model calibration is crucial for high-quality OPC results and low edge placement error (EPE) error in semiconductor fabrication. Pattern coverage check is also desired with the value to identify potential anomaly before mask tape out for monitoring and repair. This study evaluates pattern diversity based selection and pattern coverage check for Extreme Ultraviolet (EUV) C/H mask layers. Pattern diversity based selection has the advantage of incorporating information related to lithographic contrast and illumination effects, offering a more nuanced representation of patterns in a lithographic context. Using unsupervised machine learning, we analyze the lithographic pattern representations from sample designs and select out pattern representatives for OPC model. The study concludes pattern selection and coverage check can enhance model prediction performance for the OPC applications.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Keonwoo Bae, Taekyum Kim, Sanghwa Lee, Bongkeun Kim, Seongtae Jeong, Jenny Tang, Qian Zhao, Yiqiong Zhao, Chang-Il Choi, Jiao Liang, Uwe Paul Schroeder, and Mark Simmons "Advanced pattern selection and coverage check for computational lithography", Proc. SPIE 12957, Advances in Patterning Materials and Processes XLI, 129571M (22 April 2024); https://doi.org/10.1117/12.3009845
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KEYWORDS
Calibration

Optical proximity correction

Extreme ultraviolet

Data modeling

Education and training

Advanced patterning

Performance modeling

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