SePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision
cs.CV, cs.AI
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: 7 pages, 8 figures. Accepted at the 63rd ACM/IEEE Design Automation Conference (DAC 2026)
Code: https://github.com/meowrowan/SePArate
License: http://creativecommons.org/licenses/by/4.0/
The gist: In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale.
Terminology
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.
Sources
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