A strengthen mask r-CNN method for PFA image measurement

Tung-Yu Wu; Chun Yen Liao; Chun-Hung Lin; Kao-Tsai Tsai; Jun-Sheng Wu; Chao-Yi Huang

doi:10.1117/12.2551686

20 March 2020 A strengthen mask r-CNN method for PFA image measurement

Tung-Yu Wu, Chun Yen Liao, Chun-Hung Lin, Kao-Tsai Tsai, Jun-Sheng Wu, Chao-Yi Huang

Author Affiliations +

Proceedings Volume 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV; 113252G (2020) https://doi.org/10.1117/12.2551686
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

Critical dimension analysis of cross-section image with delicate accuracy has become important demand for semiconductor manufacturing. In traditional analytic method, manual measurements always accompany large deviation and lower measured efficiency. Therefore, a robust and reliable analysis method is most essential objective to obtain accurate dimensions from PFA results. In this work, we demonstrate an intelligent image analysis method which is combined Mask Region based Convolution Neural Networks (Mask r-CNN) and image processing technique. Compared with manual measurement, intelligent image analysis method can achieve significant improvement on measured results in reproducibility, repeatability, and efficiency. This intelligent image analysis will provide novel applications in CD measurement, wafer defect analysis, and focus-exposure process window judgment.

Citation Download Citation

Tung-Yu Wu, Chun Yen Liao, Chun-Hung Lin, Kao-Tsai Tsai, Jun-Sheng Wu, and Chao-Yi Huang "A strengthen mask r-CNN method for PFA image measurement", Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113252G (20 March 2020); https://doi.org/10.1117/12.2551686

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