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Sensors  2010 

Real-Time Plasma Process Condition Sensing and Abnormal Process Detection

DOI: 10.3390/s100605703

Keywords: process/equipment fault detection, spectrum, optic emission spectrum

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Abstract:

The plasma process is often used in the fabrication of semiconductor wafers. However, due to the lack of real-time etching control, this may result in some unacceptable process performances and thus leads to significant waste and lower wafer yield. In order to maximize the product wafer yield, a timely and accurately process fault or abnormal detection in a plasma reactor is needed. Optical emission spectroscopy (OES) is one of the most frequently used metrologies in in-situ process monitoring. Even though OES has the advantage of non-invasiveness, it is required to provide a huge amount of information. As a result, the data analysis of OES becomes a big challenge. To accomplish real-time detection, this work employed the sigma matching method technique, which is the time series of OES full spectrum intensity. First, the response model of a healthy plasma spectrum was developed. Then, we defined a matching rate as an indictor for comparing the difference between the tested wafers response and the health sigma model. The experimental results showed that this proposal method can detect process faults in real-time, even in plasma etching tools.

References

[1]  Hong, S.J.; May, G.S. Neural Network Based Time Series Modeling of Optical Emission Spectroscopy Data for Fault Detection in Reactive Ion Etching. Proceeding of the SPIE Process and Materials Characterization and Diagnostics in IC Manufacturing, Santa Clara, CA, USA, February 2003; pp. 1–8.
[2]  Almgren, C. The Role of RF Measurements in Plasma Etching. Semiconduct. Int?1997, 20, 99–104.
[3]  Roland, J.; Marcoux, P.; Ray, G.; Rnakin, G. Endpoint Detection in Plasma Etching. J. Vac. Sci. Technol. A?1985, 3, 631–636, doi:10.1116/1.572966.
[4]  Shadmehr, R.; Angell, D.; Chou, P.B.; Oehrlein, G.S.; Jaffe, R. Principal Component Analysis of Optical Emission Spectroscopy and Mass Spectrometry: Application to Reactive Ion Etch Process Parameter Estimation Using Neural Networks. Electrochem. Soc?1992, 139, 907–915, doi:10.1149/1.2069323.
[5]  Hong, S.J.; May, G.S. Neural Network Modeling of Reactive Ion Etching Using Principal Component Analysis of Optical Emission Spectroscopy Data. Proceedings of the Advance Semiconductor Manufacturing Conference, Boston, MA, USA, April 2002.
[6]  Yue, H.H.; Qin, S.J.; Markle, R.J.; Nauert, C.; Gatto, M. Fault Detection of Plasma Etchers Using Optical Emission Spectra. IEEE Trans. Semiconduct. Manuf?2000, 13, 374–385, doi:10.1109/66.857948.
[7]  Hong, S.J.; May, G.S. Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching. IEEE Trans. Ind. Electron?2005, 52, 1063–1072, doi:10.1109/TIE.2005.851663.
[8]  Gallagher, N.B.; Wise, B.M. Development and Benchmarking of Multivariate Statistical Process Control Tools for A Semiconductor Etch Process: Improve Robustness Through Model Updating. Proceeding of the Safeprocess, Manson, WA, USA, August 1997.
[9]  May, G.S.; Spanos, C.J. Automated Malfunction Diagnosis of Semiconductor Fabrication Equipment: A Plasma Etch Application. IEEE Trans. Semiconduct. Manuf?1993, 6, 28–40, doi:10.1109/66.210656.
[10]  Gardner, M.M.; Lu, J.C.; Gyurcsik, R.S.; Wortman, J.J.; Hornung, B.E.; Heinisch, H.H.; Rying, E.A.; Rao, S.; Davis, J.C.; Mozumder, P.K. Equipment Fault Detection Using Spatial Signatures. IEEE Trans. Compon. Pack. Man. C?1997, 20, 295–304, doi:10.1109/95.623024.
[11]  Wise, B.M.; Gallagher, N.B.; Martin, E.B. Application of PARAFAC2 to Fault Detection and Diagnosis in Semiconductor Etch. Chemometrics?2001, 15, 285–298, doi:10.1002/cem.689.
[12]  Lada, E.K.; Lu, J.C.; Wilson, J.R. A Wavelet-based Procedure for Process Fault Detection. IEEE Trans. Semiconduct. Manuf?2002, 15, 79–90, doi:10.1109/66.983447.
[13]  Anderson, H.; Gunther, S.; Fry, Bob. Plasma Etch Endpoint and Fault Detection Along with UV-Vis Absorption Spectroscopy from a Single Compact Solid State Detector. Proceeding of American Physical Society, 54th Annual Gaseous Electronics Conference; Pennsylvania State University: State College, PA, USA, 2001.
[14]  Irving, S.M. A Dry Photoresist Removal Method. Proceedings of the Kodak Photoresist Seminar, Los Angeles, CA, USA; 1968.
[15]  Bonham, H.B. Plasma Cleaning for Improved Wire Bonding on Thin-film Hybrids. Proceeding of Electronic Packaging and Production, Settle, WA, USA, February 1979.
[16]  Lieberman, M.A.; Lichtenberg, A.J. Principles of Plasma Discharges and Materials Processing, 2nd ed ed.; Wiley: New York, NY, USA, 1994.
[17]  Lieberman, M.A.; Gottscho, R.A.; Francombe, M.; Vossen, J. Design of High Density Plasma Sources for Materials Processing in Physics of Thin Films; Academic Press: New York, NY, USA, 1993.
[18]  Roger, B.; Berry, M.; Turlik, I.; Garrow, P.; Castillo, D. Soft Mask for via Patterning in Benzocyclobutene. J. Microcircuits Electron. Packaging?1994, 17, 210–218.
[19]  Goodlin, B.E.; Carsten, S.; Guenther, E. Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools. J. Electrochem. Soc?2003, 150, 778–784, doi:10.1149/1.1623772.
[20]  Francisco, M.; Scullin, P.; Scanlan, J. Broadband RF Process–State Sensor for Fault Detection and Classification. Proceeding of The International Society for Optical Engineering, San Jose, CA, USA, March 2005.
[21]  Baek, K.H.; Jung, Y.; Min, G.J.; Kang, C.; Han, K.C.; Joo, T.M. Chamber Maintenance and Fault Detection Technique for a Gate Etch Process via Self-excited Electron Resonance Spectroscopy. J. Vac. Sci. Technol. B?2005, 23, 125–129, doi:10.1116/1.1839913.
[22]  Mahadevana, S.; Shah, S.L. Fault Detection and Diagnosis in Process Data Using One-Class Support Vector Machines. J. Process Control?2009, 19, 1627–1639, doi:10.1016/j.jprocont.2009.07.011.
[23]  Qin, S.J.; Cherry, G.; Good, R.; Wang, J.; Harrison, C.A. Semiconductor Manufacturing Process Control and Monitoring: A Fab-wide Framework. J. Process Control?2006, 16, 179–191, doi:10.1016/j.jprocont.2005.06.002.
[24]  Silva, K.M.; Souza, B.A.; Brito, N.S.D. Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN. IEEE Trans. Power Delivery?2006, 21, 2058–2063, doi:10.1109/TPWRD.2006.876659.
[25]  Rojas1, A.; Nandi, A.K. Practical Scheme for Fast Detection and Classification of Rolling-Element Bearing Faults Using Support Vector Machines. Mech. Syst. Signal Process?2006, 20, 1523–1536, doi:10.1016/j.ymssp.2005.05.002.
[26]  Verron, S.; Tiplica, T.; Kobi, A. Fault Detection and Iidentification with a New Feature Selection Based on Mutual Information. J. Process Control?2008, 18, 479–490, doi:10.1016/j.jprocont.2007.08.003.
[27]  Christensen, A.L.; O’Grady, R.; Birattari, M.; Dorigo, M. Fault Detection in Autonomous Robots Based on Fault Injection and Learning. Auton. Robot?2008, 24, 49–67, doi:10.1007/s10514-007-9060-9.

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