Implementing virtual metrology for in-line quality control in semiconductor manufacturing

References

• Alexander , EB . 2004 . Optical CD Metrology Provides Sub-Nanometer Accuracy . Semiconductor International , 27 : 36 – 36 .
   Google Scholar
• Byrne , BM . 1998 . Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming , Mahwah, NJ : Lawrence Erlbaum Associates .
   Google Scholar
• Chang , YJ , Kang , Y , Hsu , CL , Chang , CT and Chan , TY . 2006 . “ Virtual Metrology Technique for Semiconductor Manufacturing ” . In Proceedings of 2006 International Joint Conference on Neural Networks 5289 – 5293 .
   Google Scholar
• Cheng , FT and Chang , YC . 2005 . “ Application Development of Virtual Metrology in Semiconductor Industry ” . In Proceedings of the 31st Annual Conference of the IEEE Industrial Electronics , 124 – 129 . North Carolina : Raleigh .
   Google Scholar
• Cheng , FT , Chen , YT , Su , YC and Zeng , DL . 2008 . Methods for Evaluating Reliance Level of a Virtual Metrology System . IEEE Transactions on Semiconductor Manufacturing , 21 : 92 – 103 .
   Web of Science ®Google Scholar
• Cheng , FT , Huang , HC and Kao , CA . 2007 . Dual-Phase Virtual Metrology Scheme . IEEE Transactions on Semiconductor Manufacturing , 20 : 566 – 571 .
   Web of Science ®Google Scholar
• Chou , S and Wang , MC . 2007 . Simulated Annealing Procedure for On-Line Control with Process Dynamics . International Journal of Systems Science , 38 : 677 – 688 .
   Web of Science ®Google Scholar
• Cudeck , R and Browne , MW . 1983 . Cross-Validation of Covariance Structures . Multivariate Behavioral Research , 18 : 147 – 167 .
   PubMed Web of Science ®Google Scholar
• DeLurgio , SA . 1998 . Forecasting Principles and Applications , New York : McGraw-Hill .
   Google Scholar
• Geldadi , P and Kowalski , B . 1986 . Partial Least-Squares Regression: A Tutorial . Analytica Chemica , 185 : 1 – 17 .
   Web of Science ®Google Scholar
• Halvorson , GD and Chou , YM . 1998 . SPC: When Every Equipment use Changes the Expected Result . Future Fab International , 1 : 161
   Google Scholar
• Hossain , A , Choudhury , ZA and Suyut , S . 1996 . Statistical Process Control of An Industrial Process in Real-Time . IEEE Transactions on Industry Applications , 32 : 243 – 249 .
   Web of Science ®Google Scholar
• Huang , CH and Lin , YN . 2002 . Decision Rule of Assignable Causes Removal Under An SPC-EPC Integration System . International Journal of Systems Science , 33 : 855 – 867 .
   Web of Science ®Google Scholar
• Hung , MH , Lin , TH , Cheng , FT and Lin , RC . 2007 . A Novel Virtual Metrology Scheme for Predicting CVD Thickness in Semiconductor Manufacturing . IEEE/ASME Transactions on Mechatronics , 12 : 308 – 316 .
   Web of Science ®Google Scholar
• John , AS , Lin , KC , Matt , R and Uzi , L . 2004 . Practical, Real-Time Multivariate FDC . Semiconductor International , 27 : 51 – 56 .
   Google Scholar
• Kittler , R . 2000 . “ Challenges for use of statistical software tools in the Semiconductor Industry ” . In Proceedings of Joint Statistical Meeting , IN : Indianapolis . Available at: http://www.ydyn.com/pubs/2000/JSM2000.pdf
   Google Scholar
• Khan , AA , Moyne , JR and Tilbury , DM . 2007 . An Approach for Factory-Wide Control Utilising Virtual Metrology . IEEE Transactions on Semiconductor Manufacturing , 20 : 364 – 375 .
   Web of Science ®Google Scholar
• Lin , TH , Cheng , FT , Hung , MH , Ye , AJ and Wu , WM . 2008 . “ A novel key-variable sifting algorithm for virtual metrology ” . In Proceedings of 2008 IEEE International Conference on Robotics and Automation , 3636 – 3641 . California : Pasadena .
   Google Scholar
• Lin , JS , Chen , PH , Wu , S , Ko , F , Zhou , MS , Liang , MS , Zou , J , Mullins , T , Moyne , J and Edwards , K . 2003 . “ Model-based wafer-to-wafer control for Cu CMP ” . In VLSI Multilevel Interconnect Conference 2003 , CA : Marina del Ray .
   Google Scholar
• Monahan , KM . 2005 . “ Enabling DFM and APC Strategies at the 32 nm Technology Node ” . In IEEE International Symposium on Semiconductor Manufacturing 398 – 401 .
   Google Scholar
• Montgomery , DC and Peck , EA . 1992 . Introduction to Linear Regression Analysis , New York : John Wiley & Sons .
   Google Scholar
• Nelson , L.S. 1984 . The shewhart control chart: Tests for special causes . Journal of Quality Technology , 16 : 237 – 239 .
   Web of Science ®Google Scholar
• Park , C . 2007 . An Algorithm For the Properties of the Integrated Process Control with Bounded Adjustments and EWMA Monitoring . International Journal of Production Research , 45 : 5571 – 5587 .
   Web of Science ®Google Scholar
• Singer , P . 2003a . E-Manufacturing: The Evolution Continues . Semiconductor International , 26 : 58 – 62 .
   Google Scholar
• Singer , P . 2003b . ‘Measuring CDs with SEMs and Scatterometers’ . Semiconductor International , http://www.semiconductor.net/article/CA 288848.html
   Google Scholar
• Su , YC , Cheng , FT , Hung , MH and Huang , HC . 2006 . Intelligent prognostics system design and implementation . IEEE Transactions on Semiconductor Manufacturing , 19 : 195 – 207 .
   Web of Science ®Google Scholar
• Western Electric Company . 1958 . Statistical Quality Control Handbook , Indianapolis, IN : Western Electric Co .
   Google Scholar
• Williams , R , Gudmunsson , D , Monahan , K and Shanthikumar , JG . 1999 . “ Optimized Sample Planning for Wafer Defect Inspection ” . In Proceedings of IEEE International Symposium on Semiconductor Manufacturing Conference , 43 – 46 . NJ : Piscataway .
   Google Scholar