FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
<|reference_start|>FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries: Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked unparalleled 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 question-answering (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 (SEM-WaD) show that our FabGPT achieves significant performance improvement in wafer defect detection and knowledge querying.<|reference_end|>
@article{jiang2024fabgpt:,
    title={FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect
  Knowledge Queries},
    author={Yuqi Jiang, Xudong Lu, Qian Jin, Qi Sun, Hanming Wu, Cheng Zhuo},
    journal={arXiv preprint arXiv:2407.10810},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10810},
    primaryClass={cs.CV cs.AI cs.AR cs.LG}
}