Problem
Research questions and friction points this paper is trying to address.
defect analysis
semiconductor manufacturing
automated inspection
root-cause analysis
process optimization
SePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision
🤖 AI Summary
为解决半导体制造中缺陷分析自动化不足的问题，提出了一种基于弱监督的晶圆缺陷分割方法SePArate，通过三阶段训练实现精确分割。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
weak supervision
wafer defect segmentation
three-phase training
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