FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
This work addresses wafer defect analysis for semiconductor fabrication, but it appears incremental as it adapts existing LMMs to a specific domain.
The authors tackled the problem of wafer defect detection and knowledge querying in integrated circuit fabrication by introducing FabGPT, a customized large multimodal model, which achieved significant performance improvements on in-house data.
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.