Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Semiconductor Process Inspection Using Mask R-CNN

Mask R-CNN을 활용한 반도체 공정 검사

  • Han, Jung Hee (Graduate School of Convergence Science and Technology(GSCST), Seoul National University) ;
  • Hong, Sung Soo (Department of Electrical and Computer Engineering, Seoul National University)
  • 한정희 (서울대학교 융합과학기술대학원 융합과학부) ;
  • 홍성수 (서울대학교 전기.정보공학부)
  • Received : 2020.08.05
  • Accepted : 2020.08.27
  • Published : 2020.09.30
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Abstract

In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.

Keywords

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