Two-Stage Vision-Language Framework Improves Semiconductor Lithography Defect Detection
Researchers propose a two-stage vision-language model that combines initial defect detection with a refinement module to improve accuracy in semiconductor lithography inspection. The approach uses Qwen3-VL fine-tuned with LoRA in the first stage, then trains a second stage to correct common errors like false positives and missed defects. This method addresses a critical quality control challenge in semiconductor manufacturing where reliable detection of small pattern defects is essential.
The study presents a failure-aware refinement framework for detecting defects in semiconductor lithography patterns, including bridges, burrs, pinches, and contamination. The first stage fine-tunes a vision-language model (Qwen3-VL) with LoRA adaptation to predict defect counts, categories, and bounding boxes from lithography images. Recognizing that direct fine-tuning produces systematic errors at test time, the researchers developed a second stage that learns specifically from first-stage failures, training a refinement module on cases where the initial adapter made mistakes. This two-stage approach demonstrates improved defect inference compared to single-stage fine-tuning alone, offering a practical solution for enhancing reliability in automated lithography inspection systems.
What's missing
The paper does not provide quantitative performance metrics (e.g., precision, recall, F1 scores) comparing the two-stage approach against single-stage baselines or existing defect detection methods. Specific details on the size and composition of the training dataset, the types of lithography processes tested, and generalization to different semiconductor manufacturing conditions are not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
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