JSTS:Journal of Semiconductor Technology and Science

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Real-time In-situ Plasma Etch Process Monitoring for Sensor Based-Advanced Process Control

  • Received : 2010.11.18
  • Accepted : 2011.03.03
  • Published : 2011.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

To enter next process control, numerous approaches, including run-to-run (R2R) process control and fault detection and classification (FDC) have been suggested in semiconductor manufacturing industry as a facilitation of advanced process control. This paper introduces a novel type of optical plasma process monitoring system, called plasma eyes chromatic system (PECSTM) and presents its potential for the purpose of fault detection. Qualitatively comparison of optically acquired signal levels vs. process parameter modifications are successfully demonstrated, and we expect that PECSTM signal can be a useful indication of onset of process change in real-time for advanced process control (APC).

Keywords

References

  1. J. Moyne, H. Haji, K. Beatty, R. Lewandowski, "SEMI E133-The Process Control System Standard: Deriving a Software Interoperability Standard for Advanced Process Control in Semiconductor Manufacturing," IEEE Trans. Semi. Manufac., vol. 30, vo. 4, pp. 408-420, 2007. https://doi.org/10.1109/TSM.2007.907617
  2. S. J. Qin, G. Cherry, R. Good, J. Wang, and C.A. Harrison, “Semiconductor Manufacturing Process Control and Monitoring: A Fab-wide Framework,” J. of Process Control, vol. 16, pp. 171-191, 2006.
  3. P. Kang, H. Lee, S. Cho, D. Kim, J. Park, C. Park, and S. Doh, “A Virtual Metrology System for Semiconductor Manufacturing,” Expert Systems with Applications, vol. 39, pp. 12554-12561, 2009.
  4. J. Ahn, D. Ryu, J. Oh, and S. Hong, “Real-Time In-Situ Plasma Etch Process Monitoring for Sensor-Based Process Control Using $PECS^{TM}$,” In Proc. 17th Korean Semiconductor Conference, Busan, Korea, Feb. 2010.
  5. H. H. Yue, S. J. Qin, J. Wiseman, and A. Toprac, "Plasma Etching Endpoint Detection Using Multiple Wavelengths for small-open area wafers," IEEE Trans. Semi. Manufac., vol. 19, no. 1, pp. 66-75, 2001. https://doi.org/10.1116/1.1331294
  6. S. Hong and G. May, “Neural Network-Based Real-Time Malfunction Diagnosis of Reactive Ion Etching Using In-situ Metrology Data,” IEEE Trans. Semi. Manufac., vol. 17, no. 3, pp. 408-421, 2004. https://doi.org/10.1109/TSM.2004.831952