用图像级标注实现晶圆缺陷像素级分割，提升缺陷分析效率
SePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision
- 仅需图像级标签，通过三阶段训练实现像素级缺陷分离
- 在合成混合缺陷数据上训练，分割准确率优于基线方法
- 适合晶圆制造中缺乏精细标注的缺陷分析场景
在半导体制造中，缺陷分析至关重要，但人工检测难以扩展。现有自动化方法在根因分析和工艺优化方面仍不足。为此，我们提出SePArate，一种弱监督晶圆缺陷分割方法。SePArate仅需图像级标注即可实现像素级图案分离，包含三阶段训练：编码器预训练、空间线索知识迁移、在合成混合缺陷数据上训练以获得精确分割。实验表明，SePArate性能优于基线方法。
原文摘要 · Abstract (English)
In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale. However, existing automated inspection methods remain insufficient for root-cause analysis and process optimization. To this end, we present SePArate, a weakly supervised wafer defect segmentation method. SePArate enables pixel-level separation of patterns by leveraging only image-level annotations. It consists of a three-phase training: encoder pretraining, knowledge transfer to learn spatial cues, and training on synthetic mixed-defect data for accurate segmentation. Experiments demonstrate that SePArate outperforms the baselines.
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