Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Semi-Supervised Learning for Fault Detection and Classification of Plasma Etch Equipment

준지도학습 기반 반도체 공정 이상 상태 감지 및 분류

  • Lee, Yong Ho (Department of Electronics Engineering, Myongji University) ;
  • Choi, Jeong Eun (Department of Electronics Engineering, Myongji University) ;
  • Hong, Sang Jeen (Department of Electronics Engineering, Myongji University)
  • 이용호 (명지대학교 공과대학 전자공학과) ;
  • 최정은 (명지대학교 공과대학 전자공학과) ;
  • 홍상진 (명지대학교 공과대학 전자공학과)
  • Received : 2020.12.16
  • Accepted : 2020.12.21
  • Published : 2020.12.31
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Abstract

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

Keywords

References

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