SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering

被引:0
|
作者
Hwang, MinJin [1 ,2 ]
Dey, Bappaditya [1 ]
Dehaerne, Enrique [1 ,3 ]
Halder, Sandip [1 ]
Shin, Young-Han [2 ]
机构
[1] imec, Kapeldreef 75, B-3001 Leuven, Belgium
[2] Univ Ulsan, Dept Phys, Ulsan, South Korea
[3] Katholieke Univ Leuven, Dept Comp Sci, Leuven, Belgium
关键词
semiconductor defect inspection; metrology; lithography; stochastic defects; supervised learning; deep learning; defect classification; defect localization; defect mask segmentation; mask r-cnn; pointrend;
D O I
10.1117/12.2657555
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by image rendering in computer graphics, a new image segmentation method that can generate high-resolution segmentation masks. It can also be flexibly integrated into common instance segmentation meta-architecture such as Mask-RCNN and semantic meta-architecture such as FCN. We implemented a model, termed as SEMI-PointRend, to generate precise segmentation masks by applying the PointRend neural network module. In this paper, we focus on comparing the defect segmentation predictions of SEMI-PointRend and Mask-RCNN for various defect types (line-collapse, single bridge, thin bridge, multi bridge non-horizontal). We show that SEMI-PointRend outperforms Mask R-CNN by up to 18.8% in terms of segmentation mean average precision.
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页数:7
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