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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.10713 |
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| _version_ | 1866911002172325888 |
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| author | Cantú, Emílio Dolgener Wittmann, Rolf Klemens Abdeen, Oliver Wagner, Patrick Samek, Wojciech Baier, Moritz Lapuschkin, Sebastian |
| author_facet | Cantú, Emílio Dolgener Wittmann, Rolf Klemens Abdeen, Oliver Wagner, Patrick Samek, Wojciech Baier, Moritz Lapuschkin, Sebastian |
| contents | Quality management in semiconductor manufacturing often relies on template matching with known golden standards. For Indium-Phosphide (InP) multi-project wafer manufacturing, low production scale and high design variability lead to such golden standards being typically unavailable. Defect detection, in turn, is manual and labor-intensive. This work addresses this challenge by proposing a methodology to generate a synthetic golden standard using Deep Neural Networks, trained to simulate photo-realistic InP wafer images from CAD data. We evaluate various training objectives and assess the quality of the simulated images on both synthetic data and InP wafer photographs. Our deep-learning-based method outperforms a baseline decision-tree-based approach, enabling the use of a 'simulated golden die' from CAD plans in any user-defined region of a wafer for more efficient defect detection. We apply our method to a template matching procedure, to demonstrate its practical utility in surface defect detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Cantú, Emílio Dolgener Wittmann, Rolf Klemens Abdeen, Oliver Wagner, Patrick Samek, Wojciech Baier, Moritz Lapuschkin, Sebastian Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing Quality management in semiconductor manufacturing often relies on template matching with known golden standards. For Indium-Phosphide (InP) multi-project wafer manufacturing, low production scale and high design variability lead to such golden standards being typically unavailable. Defect detection, in turn, is manual and labor-intensive. This work addresses this challenge by proposing a methodology to generate a synthetic golden standard using Deep Neural Networks, trained to simulate photo-realistic InP wafer images from CAD data. We evaluate various training objectives and assess the quality of the simulated images on both synthetic data and InP wafer photographs. Our deep-learning-based method outperforms a baseline decision-tree-based approach, enabling the use of a 'simulated golden die' from CAD plans in any user-defined region of a wafer for more efficient defect detection. We apply our method to a template matching procedure, to demonstrate its practical utility in surface defect detection. |
| title | Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2506.10713 |