Two-Stage Vision-Language Framework Improves Semiconductor Lithography Defect Detection
Researchers have proposed a two-stage vision-language model framework that detects and then self-corrects predictions of semiconductor lithography defects such as bridges, burrs, pinches, and contamination. The system fine-tunes Qwen3-VL with LoRA in a first stage, then trains a second refinement module specifically on the first stage's failures and their corrected labels. The approach aims to reduce false positives, missed defects, and misclassified defect types that persist after standard single-stage fine-tuning.
A preprint submitted to arXiv introduces a failure-aware refinement pipeline for detecting pattern defects in semiconductor lithography inspection, a quality-control step critical to chip manufacturing. In the first stage, the Qwen3-VL vision-language model is fine-tuned using Low-Rank Adaptation (LoRA) to predict defect counts, categories, and normalized bounding boxes directly from lithography images. Despite this fine-tuning, the authors note that common test-time errors—false positives, missed defects, and incorrect defect classifications—still occur. To address this, a second-stage refinement module is trained exclusively on the failure cases produced by the first stage, paired with their corrected ground-truth labels, enabling the system to review and revise initial outputs. The authors argue that learning from the first stage's specific mistakes allows the combined framework to surpass the accuracy achievable through single-stage fine-tuning alone. The paper is six pages with three figures and has been submitted under the Computer Vision and Pattern Recognition category.
What's missing
The dataset used for training and evaluation—its size, source, and representativeness across defect types—is not described in the abstract. It is also unclear whether the refinement module generalizes to defect types or imaging conditions not seen during training, and no ablation studies or failure-mode analyses are mentioned.
What different sources said
- arXiv cs.AICenter
Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
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