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.
机构:
Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South KoreaKorea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South Korea
Lee, Hyuck
Kim, Heeyoung
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Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South KoreaKorea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South Korea
机构:
Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South KoreaKorea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South Korea
Lee, Hyuck
Lee, Jaehyun
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机构:
Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South KoreaKorea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South Korea
Lee, Jaehyun
Kim, Heeyoung
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机构:
Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South KoreaKorea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon 34141, South Korea