一种检索增强大模型驱动的晶圆图缺陷语义推理方法
By constructing multi-source datasets and structured knowledge graphs, and combining deformable convolutional neural networks and large language models, the semantic interpretation and multi-role decision-making problems of wafer image defect detection are solved, achieving high-confidence recognition and adaptive interpretation, which is suitable for multi-role collaborative manufacturing scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGHUA UNIV
- Filing Date
- 2026-03-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing wafer image defect detection methods lack semantic interpretation capabilities, making it difficult to utilize process knowledge for causal analysis and failing to meet the differentiated decision-making needs of multiple engineering roles.
We construct a multi-source dataset, extract a structured knowledge graph, use deformable convolutional neural networks to extract visual features, combine a large language model to enhance semantic reasoning, and generate adaptive semantic interpretations.
It achieves high-confidence identification of wafer image defects and adaptive interpretation by multiple roles, possesses good interpretability and knowledge generalization ability, and supports intelligent question answering in multi-role collaborative manufacturing scenarios.
Smart Images
Figure CN121787596B_ABST