Optics: measuring and testing – Dimension
Reexamination Certificate
2008-05-13
2008-05-13
Nguyen, Sang H. (Department: 2886)
Optics: measuring and testing
Dimension
C356S601000, C702S179000, C702S189000
Reexamination Certificate
active
07372583
ABSTRACT:
A fabrication tool can be controlled using a support vector machine. A profile model of the structure is obtained. The profile model is defined by profile parameters that characterize the geometric shape of the structure. A set of values for the profile parameters is obtained. A set of simulated diffraction signals is generated using the set of values for the profile parameters, each simulated diffraction signal characterizing the behavior of light diffracted from the structure. The support vector machine is trained using the set of simulated diffraction signals as inputs to the support vector machine and the set of values for the profile parameters as expected outputs of the support vector machine. After the support vector machine has been trained, a fabrication process is performed using the fabrication tool to fabricate the structure on the wafer. A measured diffraction signal off the structure is obtained. The measured diffraction signal is inputted into the trained support vector machine. Values of profile parameters of the structure are obtained as an output from the trained support vector machine. One or more process parameters or equipment settings of the fabrication tool are adjusted based on the obtained values of the profile parameters.
REFERENCES:
patent: 5479573 (1995-12-01), Keeler et al.
patent: 5793480 (1998-08-01), Lacey et al.
patent: 5889593 (1999-03-01), Bareket
patent: 5926690 (1999-07-01), Toprac et al.
patent: 6023327 (2000-02-01), Shabde et al.
patent: 6192103 (2001-02-01), Wormington et al.
patent: 6304999 (2001-10-01), Toprac et al.
patent: 6383824 (2002-05-01), Lensing
patent: 6383888 (2002-05-01), Stirton
patent: 6433871 (2002-08-01), Lensing et al.
patent: 6451621 (2002-09-01), Rangarajan et al.
patent: 6597463 (2003-07-01), Singh et al.
patent: 6609086 (2003-08-01), Bao et al.
patent: 6625512 (2003-09-01), Goodwin
patent: 6633831 (2003-10-01), Nikoonahad et al.
patent: 6643557 (2003-11-01), Miller et al.
patent: 6650422 (2003-11-01), Singh et al.
patent: 6657736 (2003-12-01), Finarov et al.
patent: 6665446 (2003-12-01), Kato
patent: 6701206 (2004-03-01), Markle et al.
patent: 6704661 (2004-03-01), Opsal et al.
patent: 6708075 (2004-03-01), Sonderman et al.
patent: 6756243 (2004-06-01), Pasadyn et al.
patent: 6771356 (2004-08-01), Lyons et al.
patent: 6782337 (2004-08-01), Wack et al.
patent: 6785638 (2004-08-01), Niu et al.
patent: 6891626 (2005-05-01), Niu et al.
patent: 6895295 (2005-05-01), Grover et al.
patent: 6943900 (2005-09-01), Niu et al.
patent: 6999254 (2006-02-01), Phan et al.
patent: 7042569 (2006-05-01), Sezginer et al.
patent: 7043397 (2006-05-01), Johnson
patent: 7065423 (2006-06-01), Prager et al.
patent: 7072049 (2006-07-01), Niu et al.
patent: 7092110 (2006-08-01), Balasubramanian et al.
patent: 7126700 (2006-10-01), Bao et al.
patent: 7158896 (2007-01-01), Singh et al.
patent: 7171284 (2007-01-01), Vuong et al.
patent: 7186650 (2007-03-01), Dakshina-Murthy
patent: 7216045 (2007-05-01), Doddi et al.
patent: 7224456 (2007-05-01), Phan et al.
patent: 7224471 (2007-05-01), Bischoff et al.
patent: 7280229 (2007-10-01), Li et al.
patent: 2004/0017574 (2004-01-01), Vuong et al.
patent: 2004/0267397 (2004-12-01), Doddi et al.
patent: 2005/0057748 (2005-03-01), Voung et al.
patent: 2005/0192914 (2005-09-01), Drege et al.
patent: 2005/0209816 (2005-09-01), Vuong et al.
patent: 2006/0224528 (2006-10-01), Liu et al.
patent: 2007/0185684 (2007-08-01), Voung et al.
patent: 2007/0211260 (2007-09-01), Voung et al.
patent: 0448890 (1991-10-01), None
Platt, J. C. (1999). “Fast Training of Support Vector Machines Using Sequential Minimal Optimization” Chapter 12In Advances in Kernel Methods: Support Vector Learning. Schölkopf et al. eds., MIT Press: Cambridge, MA, pp. 185-208.
Keeman,V. (2005). “Support Vector Machine—An Introduction”In Support Vector Machines: Theory and Applications. Wang, L. ed., Springer-Verlag Berlin Heidelberg: The Netherlands, pp. 1-47.
U.S. Appl. No. 11/349,773, filed Feb. 7, 2006, for Vuong et al.
U.S. Appl. No. 11/786,869, filed Apr. 12, 2007, for Jin et al.
U.S. Appl. No. 11/786,870, filed Apr. 12, 2007, for Jin et al.
Adler, C. L. et al. (Jun. 1997). “High-Order Interior Caustics Produced in Scattering of a Diagonally Incident Plane Wave by a Circular Cylinder,”Journal of the Optical Society of America A14(6):1305-1315.
Arthur, G. G. et al. (1997). “Enhancing the Development Rate Model for Optimum Simulation Capability in the Subhalf-Micron Regime,”Proceedings of SPIE3049:189-200.
Ausschnitt, C. P. (Feb. 23, 2004). “A New Approach to Pattern Metrology,”Proceedings of SPIE5375:51-65.
Benincasa, D. S. et al. P. (Apr. 1987). “Spatial Distribution of the Internal and Near-Field Intensities of Large Cylindrical and Spherical Scatterers,”Applied Optics26(7):1348-1356.
Brooks, R. A. (1999). “How to Build Complete Creatures Rather than Isolated Cognitive Simulators,” MIT, Artificial Intelligence Laboratory, 7 pages, located at <http://people.csail.mit.edu/brooks/papers/how-to-build.pdf>.
Del Jesus, M. J. et al. (Jun. 2004). “Induction of Fuzzy-Rule-Based Classifiers with Evolutionary Boosting Algorithms,”IEEE Transactions on Fuzzy Systems12(3):296-308.
Dietterich, T. G. (1997). Machine Learning Research: Four Current Directions,Al Magazine, pp. 97-136, located at <http://pages.cs.wisc.edu/˜shavlik/Dietterich—AlMag 18-04-010.pdf>.
Gahegan, M. et al. (1999). “Dataspaces as an Organizational Concept for the Neural Classification of Geographic Datasets,” GeoComputation, located at <http://www.geovista.psu.edu/sites/geocomp99/Gc99/011/gc—011.htm> visited on Aug. 14, 2007, (8 pages).
Goodridge, S. G. et al. (May 8-13, 1994). “Fuzzy Behavior Fusion for Reactive Control of an Autonomous Mobile Robot: Marge,”IEEE International Conference on Robotics and Automation, San Diego, CA, 2:1622-1627.
Haykin, S. (1999).Neural Networks. 2nd edition, M. Horton ed., Prentice Hall: Upper Saddle River, New Jersey, 9 pages (Table of Contents).
Horswill, I. D. (2000). “Conflict Resolution,” Northwestern University, Computer Science 395 Behavior-Based Robotics, 10 pages, located at <www.cs.northwestern.edu/academics/courses/special—topics/395-robotics/conflict-resolution.pdf>.
Horswill, I.D. (2000). “Functional Programming of Behavior-Based Systems,” Northwestern University, Computer Science Department and The Institute for the Learning Sciences, 11 pages, <www.cs.northwestern.edu/˜ian/grl-paper.pdf>.
International Search Report and Written Opinion mailed May 16, 2005, for PCT Application No. PCT/US04/20682 filed Jun. 25, 2004, 7 pages.
Li, L. (1996). “Formulation and comparison of two recursive matrix algorithms for modeling layered diffraction grating,”Journal of the Optical Society of America A13:1024-1035.
Lock, J. A. et al. (Oct. 2000). “Exterior Caustics Produced in Scattering of a Diagonally Incident Plane Wave by Circular Cylinder: Semiclassical Scattering Theory Analysis,”Journal of the Optical Society of America A17(10):1846-1856.
Owen, J. F. et al. (Nov. 1981). “Internal Electric Field Distributions of a Dielectric Cylinder at Resonance Wavelengths,”Optics Letters6(11):540-542.
Press, W. H. et al. (1992).Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd edition, 8 pages (Table of Contents).
Rowe, N. C. (1990). “Plan Fields and Real-World Uncertainty,”AAAI Spring Symposium on Planning in Uncertain, Unpredictable, or Changing Enviroments, located at <http://www.nps.navy.mil/Content/CS
crowe/symp90un.html> visited on Oct. 18, 2007 (4 pages).
Sarkar, M. (Oct. 8-11, 2000). “Modular Pattern Classifiers: A Brief Survey,”IEEE International Conference on Systems, Man & Cybernetics, Nashville, TN, 4:2879-2883.
U.S. Appl. No. 11/484,484, filed Jul. 10, 2006 for Madriaga
Bao Junwei
Jin Wen
Li Shifang
Madriaga Manuel
Morrison & Foerster / LLP
Nguyen Sang H.
Tokyo Electron Limited
LandOfFree
Controlling a fabrication tool using support vector machine does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Controlling a fabrication tool using support vector machine, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Controlling a fabrication tool using support vector machine will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2779585