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APC Technique and Fault Detection and Classification System in Semiconductor Manufacturing Process
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 Title & Authors
APC Technique and Fault Detection and Classification System in Semiconductor Manufacturing Process
Ha, Dae-Geun; Koo, Jun-Mo; Park, Dam-Dae; Han, Chong-Hun;
 
 Abstract
Traditional semiconductor process control has been performed through statistical process control techniques in a constant process-recipe conditions. However, the complexity of the interior of the etching apparatus plasma physics, quantitative modeling of process conditions due to the many difficult features constraints apply simple SISO control scheme. The introduction of the Advanced Process Control (APC) as a way to overcome the limits has been using the APC process control methodology run-to-run, wafer-to-wafer, or the yield of the semiconductor manufacturing process to the real-time process control, performance, it is possible to improve production. In addition, it is possible to establish a hierarchical structure of the process control made by the process control unit and associated algorithms and etching apparatus, the process unit, the overall process. In this study, the research focused on the methodology and monitoring improvements in performance needed to consider the process management of future developments in the semiconductor manufacturing process in accordance with the age of the APC analysis in real applications of the semiconductor manufacturing process and process fault diagnosis and control techniques in progress.
 Keywords
semiconductor manufacturing;Advanced Process Control (APC);fault diagnosis;Fault Detection and Classification (FDC);Virtual Metrology (VM);
 Language
Korean
 Cited by
 References
1.
D. Ha and C. Han, "Analysis of APC case study and enhancement in monitoring performance in semiconductor manufacturing process. 2015 30th ICROS Annual Conference (ICROS2015), May 2015.

2.
J. H. Lee and J. M. Lee, "Progress and challenges in control of chemical processes," Annual Review of Chemical and Biomolecular Engineering, vol. 5, pp. 383-404, Mar. 2014. crossref(new window)

3.
G. W. Rubloff, "In-situ metrology: the path to real-time advanced process control," Characterization and Metrology for ULSI Technology: 2003 International Conference, pp. 583-591, May 2003.

4.
S. J. Qin and T. A. Badgwell, "A survey of industrial model predictive control technology," Control Engineering Practice, vol. 11, pp. 733-764, Aug. 2002.

5.
M. Kano and M. Ogawa, "The state of the art in advanced chemical process control in Japan," 7th IFAC International Symposium on Advanced Control of Chemical Process, Turkey, 2009.

6.
M. Morari and J. H. Lee, "Model predictive control: Past, present and future," 6th International Symposium on Process Systems Engineering (PSE'97), 1997.

7.
H.-M. Kim, S. M. Seong, and K.H. Kang, "A statistical analysis method for image processing errors in the position alignment of BGA-type semiconductor packages," Journal of Institute of Control, Robotics and Systems (in Korean), vol.19, no.11, pp.984-990, 2013. crossref(new window)

8.
D.-S. Moon, S.-K. Kim, and S.-H. Kim, "A fault detection system for wind power generator based on intelligent clustering method," Journal of Institute of Control, Robotics and Systems (in Korean), vol.19, no.1, pp.27-33, 2013. crossref(new window)

9.
C. Kymal and P. Patiyasevi, "Semiconductor quality initiatives: how to maintain quality in this fast-changing industry," Quality Digest, vol. 26, no. 4, pp. 43-48, 2006.

10.
C. F. Chien, W. C. Wang, and J. C. Cheng, "Data mining for yield enhancement in semiconductor manufacturing and an empirical study," Expert Systems with Applications, vol. 33, no. 1, pp. 192-198, 2007. crossref(new window)