FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries

TL;DR

FabGPT is a specialized large multimodal model designed for wafer defect detection and knowledge querying in IC fabrication, combining defect detection, root cause analysis, and expert Q&A to improve accuracy and reduce subjectivity.

Contribution

The paper introduces FabGPT, a novel multimodal model tailored for wafer defect analysis, with new modules and training strategies to enhance defect detection and knowledge integration.

Findings

FabGPT significantly improves wafer defect detection accuracy.

FabGPT effectively balances defect knowledge and original knowledge in queries.

FabGPT reduces subjectivity in defect detection under complex backgrounds.

Abstract

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.