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Proceedings Paper

A strengthen mask r-CNN method for PFA image measurement
Author(s): Tung-Yu Wu; Chun Yen Liao; Chun-Hung Lin; Kao-Tsai Tsai; Jun-Sheng Wu; Chao-Yi Huang
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Paper 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.

Paper Details

Date Published: 20 March 2020
PDF: 6 pages
Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113252G (20 March 2020); doi: 10.1117/12.2551686
Show Author Affiliations
Tung-Yu Wu, Winbond Electronics Corp. (Taiwan)
Chun Yen Liao, Winbond Electronics Corp. (Taiwan)
Chun-Hung Lin, Winbond Electronics Corp. (Taiwan)
Kao-Tsai Tsai, Winbond Electronics Corp. (Taiwan)
Jun-Sheng Wu, Winbond Electronics Corp. (Taiwan)
Chao-Yi Huang, Winbond Electronics Corp. (Taiwan)


Published in SPIE Proceedings Vol. 11325:
Metrology, Inspection, and Process Control for Microlithography XXXIV
Ofer Adan; John C. Robinson, Editor(s)

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