发表机构
Seoul National University; SK Hynix（首尔大学; SK海力士）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对半导体制造中缺陷分析的规模化需求，提出弱监督晶圆缺陷分割方法SePArate，经三阶段训练后在实验中表现优于基线方法。
AI 中文摘要
在半导体制造中，缺陷分析至关重要，但人工检测无法规模化；然而现有自动检测方法仍不足以用于根因分析和工艺优化。为此，我们提出SePArate，一种弱监督晶圆缺陷分割方法，仅利用图像级标注即可实现图案的像素级分割，其训练包含三个阶段：编码器预训练、迁移知识以学习空间线索、在合成混合缺陷数据上训练以实现精准分割。实验表明，SePArate的性能优于基线方法。
英文摘要
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.
Comments7 pages, 8 figures. Accepted at the 63rd ACM/IEEE Design Automation Conference (DAC 2026)
