Defect detection in photolithographic patterns using deep learning models trained on synthetic data
In the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402501761X |
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| author | Prashant P. Shinde Priyadarshini P. Pai Shashishekar P. Adiga K. Subramanya Mayya Yongbeom Seo Myungsoo Hwang Heeyoung Go Changmin Park |
| author_facet | Prashant P. Shinde Priyadarshini P. Pai Shashishekar P. Adiga K. Subramanya Mayya Yongbeom Seo Myungsoo Hwang Heeyoung Go Changmin Park |
| author_sort | Prashant P. Shinde |
| collection | DOAJ |
| description | In the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. To resolve the problem of data unavailability, we artificially generate scanning electron microscopy (SEM) images of line patterns with known distribution of defects and autonomously annotate them. We then employ state-of-the-art object detection models to investigate defect detection performance as a function of defect size, much smaller than the pitch width. We find that the real-time object detector YOLOv8 has the best mean average precision of 96% as compared to EfficientNet, 83%, and SSD, 77%, with the ability to detect smaller defects. We report the smallest defect size that can be detected reliably. When tested on real SEM data, the YOLOv8 model correctly detected 84.6% of Bridge defects and 78.3% of Break defects across all relevant instances. These promising results suggest that synthetic data can be used as an alternative to real-world data in order to develop robust machine-learning models. |
| format | Article |
| id | doaj-art-278f913628e844a88f3f436b3ba83ee9 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-278f913628e844a88f3f436b3ba83ee92025-08-20T02:34:35ZengElsevierHeliyon2405-84402025-05-011110e4337710.1016/j.heliyon.2025.e43377Defect detection in photolithographic patterns using deep learning models trained on synthetic dataPrashant P. Shinde0Priyadarshini P. Pai1Shashishekar P. Adiga2K. Subramanya Mayya3Yongbeom Seo4Myungsoo Hwang5Heeyoung Go6Changmin Park7NextGen Projects (SAIT-India), Samsung Semiconductor India Research, Bangalore, 560048, India; Corresponding authors.NextGen Projects (SAIT-India), Samsung Semiconductor India Research, Bangalore, 560048, IndiaNextGen Projects (SAIT-India), Samsung Semiconductor India Research, Bangalore, 560048, India; Corresponding authors.NextGen Projects (SAIT-India), Samsung Semiconductor India Research, Bangalore, 560048, IndiaFoundry Process Development Team, Semiconductor R&D Center, Samsung Electronics, Seoul, KoreaFoundry Process Development Team, Semiconductor R&D Center, Samsung Electronics, Seoul, KoreaFoundry Process Development Team, Semiconductor R&D Center, Samsung Electronics, Seoul, KoreaFoundry Process Development Team, Semiconductor R&D Center, Samsung Electronics, Seoul, KoreaIn the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. To resolve the problem of data unavailability, we artificially generate scanning electron microscopy (SEM) images of line patterns with known distribution of defects and autonomously annotate them. We then employ state-of-the-art object detection models to investigate defect detection performance as a function of defect size, much smaller than the pitch width. We find that the real-time object detector YOLOv8 has the best mean average precision of 96% as compared to EfficientNet, 83%, and SSD, 77%, with the ability to detect smaller defects. We report the smallest defect size that can be detected reliably. When tested on real SEM data, the YOLOv8 model correctly detected 84.6% of Bridge defects and 78.3% of Break defects across all relevant instances. These promising results suggest that synthetic data can be used as an alternative to real-world data in order to develop robust machine-learning models.http://www.sciencedirect.com/science/article/pii/S240584402501761XScanning electron microscopyData generationMachine learningDefect detection |
| spellingShingle | Prashant P. Shinde Priyadarshini P. Pai Shashishekar P. Adiga K. Subramanya Mayya Yongbeom Seo Myungsoo Hwang Heeyoung Go Changmin Park Defect detection in photolithographic patterns using deep learning models trained on synthetic data Heliyon Scanning electron microscopy Data generation Machine learning Defect detection |
| title | Defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| title_full | Defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| title_fullStr | Defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| title_full_unstemmed | Defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| title_short | Defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| title_sort | defect detection in photolithographic patterns using deep learning models trained on synthetic data |
| topic | Scanning electron microscopy Data generation Machine learning Defect detection |
| url | http://www.sciencedirect.com/science/article/pii/S240584402501761X |
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