Image analysis – Applications – Manufacturing or product inspection
Reexamination Certificate
1997-05-30
2001-03-20
Mancuso, Joseph (Department: 2623)
Image analysis
Applications
Manufacturing or product inspection
C382S209000, C382S151000
Reexamination Certificate
active
06205239
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
This invention relates to defect classification and diagnosis of manufacturing defects.
BACKGROUND OF THE INVENTION
In most manufacturing processes, management of through-put and yield are of concern. The ability to locate potential problems, identify problems, and take corrective action to obviate the source of the defect, and if possible, to repair the defect, can make a significant difference in the performance of manufacturing process. Therefore, it is desirable to have the best systems possible for identifying possible problems or anomalies, identifying an anomaly as a particular type of defect, identifying the source of the defect, and repairing the manufactured object to correct the defect if possible. This is particularly true in the semiconductor industry.
In the semiconductor manufacturing industry, a challenge remains to improve yields as the designs get smaller and smaller. Particles and process defects can limit yields in manufacturing semiconductor devices. Therefore, systems that perform the general functions described above can become extremely important. Conventional techniques have shortcomings including less than desirable speed and accuracy. With respect to identifying defects in the manufacturing process, manual classification has been required of anomalies and manual diagnosing of the cause of defects. Such manual inputs may have resulted in inconsistent results and consumption of considerable operator time.
SUMMARY OF THE INVENTION
According to an aspect of the present invention, a method for repairing a defect on a manufactured object includes placing the manufactured device on a moveable stage; capturing and preparing a digital-pixel-based representation of the image; symbolically decomposing the digital-pixel-based representation of an image to create a primitive-based representation of the image; analyzing the primitive-based representation of the image to detect and locate an anomaly; isolating primitives associated with the anomaly; comparing the isolated primitives associated with the anomaly with primitives in a knowledgebase to locate a set of primitives in the knowledgebase that are most like the isolated primitives associated with the anomaly; assigning a defect-type label associated with the set of primitives in the knowledgebase that was most similar to the isolated primitives associated with the anomaly, and using a repair tool to repair the defect based on defect-type label for the anomaly. In one embodiment, the manufactured object is a semiconductor wafer.
According to another aspect of the present invention, a system for repairing a semiconductor device includes a computer having a processor and memory; a moveable stage for holding and positioning the semiconductor wafer; a camera for capturing an image of the wafer on the stage; a digitizer coupled to the camera for producing a digital-pixel-based representation of the image; a computer having a processor and memory, the computer coupled to the digitizer for receiving the digital-pixel-based representation from the digitizer and the computer coupled to the stage for selectively moving the stage to align the wafer, and the computer operable to: symbolically decompose the digital-pixel-based representation of an image to create a primitive-based representation of the image, analyze the primitive-based representation of the image to detect and locate any anomalies, compare primitives associated with the anomalies with sets of primitives in a knowledgebase to classify each anomaly as repairable or non-repairable, and to deliver a repair instruction to a repair tool if the anomaly is repairable; and a repair tool coupled to the computer for receiving a repair instruction therefrom and operable to perform the repair instruction. According to another aspect of the present invention, the computer is further operable to decompose the digital-pixel-based representation of an image by aligning geometric objects in the pixel-based representation of the image and is operable to align the geometric objects with respect to rotation by developing a histogram of angles and lengths and matching them to determine a rotational shift.
REFERENCES:
patent: 4131472 (1978-12-01), MacDonald, Jr. et al.
patent: 4233625 (1980-11-01), Atman
patent: 4353087 (1982-10-01), Berry et al.
patent: 4509075 (1985-04-01), Simms et al.
patent: 4550374 (1985-10-01), Meshman et al.
patent: 4579455 (1986-04-01), Levy et al.
patent: 4641257 (1987-02-01), Ayata
patent: 4791586 (1988-12-01), Maeda et al.
patent: 4794648 (1988-12-01), Ayata et al.
patent: 4907285 (1990-03-01), Nakano et al.
patent: 4955062 (1990-09-01), Terui
patent: 5042055 (1991-08-01), Wirt et al.
patent: 5048100 (1991-09-01), Kuperstein
patent: 5109430 (1992-04-01), Nishihara et al.
patent: 5144683 (1992-09-01), Suzuki et al.
patent: 5220621 (1993-06-01), Saitoh
patent: 5321770 (1994-06-01), Huttenlocher et al.
patent: 5436979 (1995-07-01), Gray et al.
patent: 5515453 (1996-05-01), Hennessey et al.
patent: 5544256 (1996-08-01), Brecher et al.
patent: 5808735 (1998-09-01), Lee et al.
“Investigation of a Fuzzy Grammar for Automated Visual Inspection,” by Hahn Kwang-Soo, Dissertation in Interdisciplinary Engineering, Graduate Faculty, Texas Tech University, Aug. 1989.
“Picture Interpretation A Symbolic Approach,” by Sandy Dance, Terry Caelli and Zhi-Qiang Liu, Series in Machine Perception and Artificial Intelligence—vol. 20, World Scientific, Apr. 1995.
“Symbolic Image Interpretation by Parsing, Interpreting, and Prunning,” by Satffan Truvé A Dissertation for the Ph.D. Degree in Computing Science at Chalmers University of Technology, Department of Computer Sciences, dated Jun. 4, 1992.
“Symbolic Reasoning Among 3-D Models and 2-D Images,” A Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy, by Rodney Allen Brooks, dated Jun. 1981.
“Symbolic Pixel Labeling for Curvilinear Feature Dectection,” Computer Vision Laboratory, Center for Automation Research, University of Maryland, by John Canning, J. John Kim, and Azriel Rosenfeld, dated Jan. 1987.
“Symbolic Learning and Interpretation of 2-D Electrophoresis Gels,” University Libraries, Carnegie Mellon University, by Pierre Nugues, Robert Whalen and Jean-Paul Haton, dated Apr. 11, 1991.
“Techniques for Syntactic Analysis of Images with Application for Automatic Visual Inspection,” Dissertation in Interdisciplinary Engineering, Graduate Faculty, Texas Tech University, by Youling Lin, Dec. 1990.
“Segmentation Through Symbolic Surface Descriptions,” Computer Vision Laboratory, Electrical Engineering and Computer Science Dept., The University of Michigan, by Paul Besl and Ramesh Jain, dated Feb. 1986.
“Zero-Crossing Symbolic Vision Primitives Emulating Physiologic Encoding Schemes,” Thesis Submitted to the Faculty of the Oregon Graduate Center in Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science & Engineering, by Daniel P. Lulich, dated Dec. 1985.
“Code Converter Designs Using Optical Symbolic Substitution Logic,” Thesis Submitted to Graduate Engineering & Research, School of Engineering, University of Dayton, In partial Fulfillment of the Requirements for the Degree Master of Science in Electrical Engineering, by Constantinos C. Agrotis, dated Jul. 1989.
“Symbolic Inverse of Discrete Gaussian Blur,” Computer Vision and robotics Laboratory, Department of Electrical Engineering, McGill University, by B. Kimia and S.W. Zucker, dated Jun. 1985.
“The Symbolic Representation, Analysis, and Manipulation of Morphological Algorithms,” A Thesis Presented to The Academic Faculty, in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electrical Engineering, Georgia Institute of Technology, by Craig Howard Richardson, dated Dec. 1991.
“Fundamentals of Electronic Image Processing,” by Arthur R. Weeks, Jr., SPIE/IEEE on Imaging Science & Engineering, not dated.
“Symbolic Projecti
Cleavelin C. Rinn
Hennessey A. Kathleen
Khaja Veera S.
Lin YouLing
Pattikonda Ramakrishna
Bali Vikkram
Mancuso Joseph
Telecky , Jr. Frederick J.
Texas Instruments Incorporated
Troike Robert L.
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