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.
引用
收藏
页数:7
相关论文
共 25 条
  • [1] Automated semiconductor wafer defect classification dealing with imbalanced data
    Lee, Po-Hsuan
    Wang, Zhe
    Teh, Cho
    Hsiao, Yi-Sing
    Fang, Wei
    METROLOGY, INSPECTION, AND PROCESS CONTROL FOR MICROLITHOGRAPHY XXXIV, 2020, 11325
  • [2] Application of Wafer Defect Pattern Classification Model in the Semiconductor Industry
    Lee, Chin-Wei
    Hladek, Daniel
    Pleva, Matus
    Liao, Yuan-Fu
    Su, Ming-Hsiang
    2023 ASIA PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE, APSIPA ASC, 2023, : 2173 - 2177
  • [3] A novel hybrid resampling for semiconductor wafer defect bin classification
    Park, You-Jin
    Pan, Rong
    Montgomery, Douglas C.
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2023, 39 (01) : 67 - 80
  • [4] Deep learning driven silicon wafer defect segmentation and classification
    Ingle, Rohan
    Shahade, Aniket K.
    Gaikwad, Mayur
    Patil, Shruti
    METHODSX, 2025, 14
  • [5] Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification
    Jo, Uk
    Bum Kim, Seoung
    IEEE ACCESS, 2025, 13 : 56 - 66
  • [6] Semiconductor Wafer Defect Recognition Based on Improved Coordinate Attention Mechanism
    He, Hao
    Wei, Yuanjie
    Lin, Xionghao
    Zhu, Minmin
    Zhang, Haizhong
    IEEE ACCESS, 2025, 13 : 46856 - 46864
  • [7] Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding
    Saad, N. H.
    Ahmad, A. E.
    Saleh, H. M.
    Hasan, A. F.
    2ND INTERNATIONAL CONFERENCE ON GREEN DESIGN AND MANUFACTURE 2016 (ICONGDM 2016), 2016, 78
  • [8] A voting-based ensemble feature network for semiconductor wafer defect classification
    Sampa Misra
    Donggyu Kim
    Jongbeom Kim
    Woncheol Shin
    Chulhong Kim
    Scientific Reports, 12
  • [9] A voting-based ensemble feature network for semiconductor wafer defect classification
    Misra, Sampa
    Kim, Donggyu
    Kim, Jongbeom
    Shin, Woncheol
    Kim, Chulhong
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [10] WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects
    Nag, Subhrajit
    Makwana, Dhruv
    Teja, Sai Chandra R.
    Mittal, Sparsh
    Mohan, C. Krishna
    COMPUTERS IN INDUSTRY, 2022, 142