Deep learning aided tool for fast and accurate segmentation of multi-part semiconductor features

Isaac Wilfried Sanou; Julien Baderot; Ali Hallal; Stéphanie Bricq; Yannick Benezeth; Franck Marzani; Sergio Martinez; Johann Foucher

doi:10.1117/12.3009287

9 April 2024 Deep learning aided tool for fast and accurate segmentation of multi-part semiconductor features

Isaac Wilfried Sanou, Julien Baderot, Ali Hallal, Stéphanie Bricq, Yannick Benezeth, Franck Marzani, Sergio Martinez, Johann Foucher

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 1295523 (2024) https://doi.org/10.1117/12.3009287
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Deep learning models have been proven to automate metrology tasks. It provides accurate, robust and fast results if it is trained with proper data. Nonetheless, obtaining training data remains tedious. It requires an expert user to delimitate objects boundaries in several images representing tens to hundreds of objects. Instead of drawing precise boundaries, we propose a tool relying on a rectangular bounding box to detect and segment objects. For complex applications with non-homogeneous background, the user must draw one box per object to segment them. For more homogeneous objects such as contacts, one box on the whole image can successfully segment all objects at once. To further improve the capabilities of the tool, we provide the possibility to segment the different material regions inside the found objects. The process's robustness is demonstrated through benchmarking in two contexts. Firstly, we trained two Mask R-CNN models, one with manual segmentations and the other with segmentations obtained using our tool. We compared the two models to the manual reference and found that the tool is consistent with human annotations while reducing annotation time by a factor of 30. Additionally, the tool greatly reduces user bias as the selected segmentation features are more stable. Furthermore, we suggest extending the tool to identify objects within the already found objects.

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

Citation Download Citation

Isaac Wilfried Sanou, Julien Baderot, Ali Hallal, Stéphanie Bricq, Yannick Benezeth, Franck Marzani, Sergio Martinez, and Johann Foucher "Deep learning aided tool for fast and accurate segmentation of multi-part semiconductor features", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 1295523 (9 April 2024); https://doi.org/10.1117/12.3009287

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