Contour metrology for process matching and OPC qualification with machine learning-based site selection

Jae Yeol Maeng; Soo Kheng Tan; Yee Mei Foong; Xuefeng Zeng; Shibing Wang; Steven Lubin; Robin Chia; Angeline Chung; Ken Jantzen; Yuyang Sun

doi:10.1117/12.3012681

9 April 2024 Contour metrology for process matching and OPC qualification with machine learning-based site selection

Jae Yeol Maeng, Soo Kheng Tan, Yee Mei Foong, Xuefeng Zeng, Shibing Wang, Steven Lubin, Robin Chia, Angeline Chung, Ken Jantzen, Yuyang Sun

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553N (2024) https://doi.org/10.1117/12.3012681
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

We present a contour metrology-based process matching flow with machine learning-based site selection for best coverage, contour comparisons, and scoring to quantify process differences. This method can significantly improve the efficiency of process technology transfers between fabs. The key technology includes: 1) high-performance ML clustering on a full chip product with hundreds of millions of anchoring points, 2) process-matching oriented custom feature engineering that drives quantitative understanding of each SEM image, and 3) stable and reliable contour extraction of large amounts of CD-SEM images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Jae Yeol Maeng, Soo Kheng Tan, Yee Mei Foong, Xuefeng Zeng, Shibing Wang, Steven Lubin, Robin Chia, Angeline Chung, Ken Jantzen, and Yuyang Sun "Contour metrology for process matching and OPC qualification with machine learning-based site selection", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553N (9 April 2024); https://doi.org/10.1117/12.3012681

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