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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.10810 |
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| _version_ | 1866910829618659328 |
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| author | Jiang, Yuqi Lu, Xudong Jin, Qian Sun, Qi Wu, Hanming Zhuo, Cheng |
| author_facet | Jiang, Yuqi Lu, Xudong Jin, Qian Sun, Qi Wu, Hanming Zhuo, Cheng |
| contents | Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked extraditionary abilities in understanding images and text, fostering intelligent fabrication. Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query. FabGPT manifests expertise in conducting defect detection in Scanning Electron Microscope (SEM) images, performing root cause analysis, and providing expert Q&A on fabrication processes. FabGPT matches enhanced multimodal features to automatically detect minute defects under complex wafer backgrounds and reduce the subjectivity of manual threshold settings. Besides, the proposed modulation module and interactive corpus training strategy embed wafer defect knowledge into the pre-trained model, effectively balancing Q&A queries related to defect knowledge and original knowledge and mitigating the modality bias issues. Experiments on in-house fab data show that FabGPT achieves significant performance improvement in wafer defect detection and knowledge querying. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10810 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries Jiang, Yuqi Lu, Xudong Jin, Qian Sun, Qi Wu, Hanming Zhuo, Cheng Computer Vision and Pattern Recognition Artificial Intelligence Hardware Architecture Machine Learning Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked extraditionary abilities in understanding images and text, fostering intelligent fabrication. Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query. FabGPT manifests expertise in conducting defect detection in Scanning Electron Microscope (SEM) images, performing root cause analysis, and providing expert Q&A on fabrication processes. FabGPT matches enhanced multimodal features to automatically detect minute defects under complex wafer backgrounds and reduce the subjectivity of manual threshold settings. Besides, the proposed modulation module and interactive corpus training strategy embed wafer defect knowledge into the pre-trained model, effectively balancing Q&A queries related to defect knowledge and original knowledge and mitigating the modality bias issues. Experiments on in-house fab data show that FabGPT achieves significant performance improvement in wafer defect detection and knowledge querying. |
| title | FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2407.10810 |