Journal of the Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회논문지)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
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Endpoint Detection Using Hybrid Algorithm of PLS and SVM

PLS와 SVM복합 알고리즘을 이용한 식각 종료점 검출

  • Lee, Yun-Keun (Department of Information and Communication Engineering & SPDRC, Myongji University) ;
  • Han, Yi-Seul (Department of Information and Communication Engineering & SPDRC, Myongji University) ;
  • Hong, Sang-Jeen (Department of Electronics Engineering, Myongji University) ;
  • Han, Seung-Soo (Department of Information and Communication Engineering & SPDRC, Myongji University)
  • 이윤근 (명지대학교 정보통신공학과) ;
  • 한이슬 (명지대학교 정보통신공학과) ;
  • 홍상진 (명지대학교 전자공학과) ;
  • 한승수 (명지대학교 정보통신공학과)
  • Received : 2011.08.09
  • Accepted : 2011.08.24
  • Published : 2011.09.01
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Abstract

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

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References

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