Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

Real Time Endpoint Detection in Plasma Etching Using Decision Making Algorithm

플라즈마 식각 공정에서 의사결정 알고리즘을 이용한 실시간 식각 종료점 검출

  • Noh, Ho-Taek (Dept. of Information and Communication Engineering, Myongji University) ;
  • Park, Young-Kook (Dept. of Information and Communication Engineering, Myongji University) ;
  • Han, Seung-Soo (Dept. of Information and Communication Engineering, Myongji University)
  • Received : 2015.11.06
  • Accepted : 2015.12.16
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The endpoint detection (EPD) is the most important technique in plasma etching process. In plasma etching process, the Optical Emission Spectroscopy (OES) is usually used to analyze plasma reaction. And Plasma Impedance Monitoring (PIM) system is used to measure the voltage, current, power, and load impedance of the supplied RF power during plasma process. In this paper, a new decision making algorithm is proposed to improve the performance of EPD in SiOx single layer plasma etching. To enhance the accuracy of the endpoint detection, both OES data and PIM data are utilized and a newly proposed decision making algorithm is applied. The proposed method successfully detected endpoint of silicon oxide plasma etching.

플라즈마 식각 공정에서 식각 종료점 검출은 중요한 요소이다. Optical Emission Spectroscopy (OES) 는 플라즈마 반응을 분석하는데 사용한다. 그리고 Plasma Impedance Monitoring (PIM) 은 플라즈마 공정 중에 RF power에 의한 voltage, current, power, impedance를 분석하는데 사용한다. 본 논문에서는 새로 제안하는 의사결정 알고리즘을 이용하여 single layer 산화막 플라즈마 식각에서 식각 종료점 검출의 성능을 향상시키는 것을 제안한다. 식각 종료점 검출의 정확도를 높이기 위해 OES 데이터와 PIM 데이터들을 의사결정 알고리즘에 모두 적용하여 사용한다. 제안된 방법은 SiOx 플라즈마 식각에서 식각 종료점을 정확하게 검출한다.

Keywords

References

  1. KIM, Seung-Gyun, et al. Endpoint Detection in Low Open Area Ratio Plasma Etching Using Hybrid Method. ECS Transactions, 44(1), pp.1081-1086, 2012.
  2. SHIN, Sung-Hwan, et al. Endpoint Detection for Single Layer Plasma Etch Using Hybrid Fuzzy Inference Systems. ECS Transactions, 60(1), pp.875-880, 2014. https://doi.org/10.1149/06001.0875ecst
  3. SMITH, Lindsay I. A tutorial on principal components analysis. Cornell University, USA, 51: 52, 2002.
  4. WHITE, David A., et al. Low open-area endpoint detection using a PCA-based T 2 statistic and Q statistic on optical emission spectroscopy measurements. Semiconductor Manufacturing, IEEE Transactions on, 13(2), pp.193-207, 2000. https://doi.org/10.1109/66.843635
  5. JEON, Sung-Ik, et al. Endpoint Detection Strategy in Bosch Process Using PCA and HMM. ECS Transactions, 44(1), pp.1087-1091, 2012.