Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Role of Features in Plasma Information Based Virtual Metrology (PI-VM) for SiO2 Etching Depth

플라즈마 정보인자를 활용한 SiO2 식각 깊이 가상 계측 모델의 특성 인자 역할 분석

  • Jang, Yun Chang (Department of Energy Systems Engineering, Seoul National University) ;
  • Park, Seol Hye (Department of Energy Systems Engineering, Seoul National University) ;
  • Jeong, Sang Min (Department of Energy Systems Engineering, Seoul National University) ;
  • Ryu, Sang Won (Department of Energy Systems Engineering, Seoul National University) ;
  • Kim, Gon Ho (Department of Energy Systems Engineering, Seoul National University)
  • 장윤창 (서울대학교 에너지시스템공학부) ;
  • 박설혜 (서울대학교 에너지시스템공학부) ;
  • 정상민 (서울대학교 에너지시스템공학부) ;
  • 유상원 (서울대학교 에너지시스템공학부) ;
  • 김곤호 (서울대학교 에너지시스템공학부)
  • Received : 2019.11.21
  • Accepted : 2019.12.05
  • Published : 2019.12.31
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Abstract

We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO2 etching depth with variation of 5% contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the explanatory power to the process results is in the order of plasma information about electron energy distribution function (PIEEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PIEEDF. Informative vector for developed PI-VM also shows relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical species that governs the etching depth can be sensitively monitored when OES features are used with PIEEDF. Securing PIEEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PIEEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and classification (FDC) can be effectively developed by using the PI-VM.

Keywords

References

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