Television – Special applications – Manufacturing
Reexamination Certificate
2000-12-05
2004-04-13
Lee, Young (Department: 2613)
Television
Special applications
Manufacturing
C348S125000
Reexamination Certificate
active
06720989
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to a computer system for inspecting objects for manufacturing defects and more particularly to an algorithm in the computer system for inspecting and analyzing periodic arrays of electronically acquired imagery of an object.
BACKGROUND OF THE INVENTION
While advances in computer processing power have made processing of large amounts of data possible, an automated optical inspection problem for arrays of geometric targets still exists. Automated optical inspection is the inspection of electronically acquired imagery of an object for tolerance, color, blemishes, cracks or a wide variety of manufacturing defects that might be present in the object. The automated optical inspection problem arises, among other places, in the inspection of aperture masks used in CRT-type color monitors and television sets. Problems encountered during aperture masks inspections also occur during inspections of other objects, which include inspection of periodic arrays of annular targets. Thus, two seemly different objects, such as flat panel displays and filters for filtering bacterial particles out of a product stream of bio-engineered vaccines and chemicals have the same inspection problem. Therefore, while the following discusses inspection of aperture masks with arrays of annular targets, it should be apparent to one of ordinary skill in the art that the invention relates to inspection of all objects with arrays of geometric targets.
Aperture masks generally are comprised of thin metal sheets perforated by hundreds of thousands of tiny holes. These holes are too small to see with an unaided eye and each hole has a precise shape, or profile. The shape of each hole varies slightly and definitely according to its position across the width and/or height of the mask. The degree to which the shape of these holes can be maintained in manufacturing of the aperture mask has a direct bearing on whether the mask can be used by a manufacturer.
Inspection of aperture masks during manufacturing is a particularly demanding problem because there are vast numbers of holes, at least several hundred thousand holes, in each mask. Aperture masks are relatively inexpensive to manufacture. Nonetheless, the shape of a single hole that is out of tolerance eventually shows up as a blemish that an end user can see in a finished product. For example, the end user will see a blemish in an image that is produced by a CRT-type color monitor, which includes the defective aperture mask. Thus, automated or manual inspection of each mask has to be performed. Automated inspection by a computer leads to a formidable data reduction problem since each hole must be covered by many pixels, thereby producing billions of pixels across the length and width of each mask.
Some manufacturers use human inspectors to manually inspect each mask but do not use any special magnification method. The human inspectors hold each mask up to the light and bend it in various ways to detect an irregular hole or area in the mask. While the manufacturing of the aperture masks is automated, inspections of the masks are performed by groups of inspectors. Each person in a group may inspect a particular section of the masks and inspections of the masks are performed at much slower rates than the rates at which the masks are manufactured. The manual inspection process also is a relatively expensive undertaking for the manufacturer.
A current automated method uses two-dimensional video cameras to analyze data on a computer. Thereafter various mathematical operations, such as edge detection, a gradient calculation, or some other type of transform to manipulate the data, are performed on the two-dimensional representation of the data. The automated method measures the inside and outside diameter of each hole to calculate whether the inner diameter is the right range of tolerance. This method generates an unusable and impractical amount of data that may only be analyzed by a very specialized and expensive computer. In an industrial environment, the masks vibrate as they emerge from the production line. This vibration is enough to make an image useless unless the image is taken over an extremely short time period.
U.S. patent application Ser. No. 09/522,685 of which this application is a continuation-in-part, relates to a system and method for inspecting electronically acquired imagery, from a one-dimensional camera, of an object for tolerance, color, blemishes, cracks or a wide variety of manufacturing defects that might be present in an object. The method to detect manufacturing defects includes an algorithm for analyzing the periodic pattern of geometric elements in an array and detecting deviations from numerical acceptance norms, such as diameter, spacing, and symmetry, for the geometric elements.
This previously filed application, moreover, discloses a method for analyzing one-dimensional image data. The process of analyzing two-dimensional images, however, is useful in fields of failure analysis and prediction, process control, machine vision and automated optical inspection, and voice recognition (especially in the presence of “noise”, data compression, vibration control and echo suppression). What is needed, therefore, is an automated algorithm for inspecting imagery of any one or two-dimensional object that can be implemented by means for parallel hardware scales at a favorable rate with limited inter-processor communication. The method must also be unaffected by the visual effects of mechanical vibration on an image while tolerating the effects of imperfect positions of the target object by an operator or as a result of equipment shortcomings.
SUMMARY OF THE INVENTION
The present invention relates to a system and method for scanning electronically acquired periodic images from an object and thereafter, inspecting the periodic images by using predetermined rules. The method to inspect the image includes an algorithm for analyzing the periodic patterns of the image and detecting deviations from numerical acceptance norms. In the system, the field of view of a camera, such as a video camera, viewing the object includes a two-dimensional image of the object. The camera captures the two-dimensional image of the object and converts the image into an array of scan lines, whereby each scan line represents a one-dimensional “slice” of target shape of the object. Hence, while all two dimensional images do not have periodic patterns, the array of scan lines represent a periodic pattern that is used by the algorithm in the inventive system. Alternatively, the camera may capture a scan line of a one-dimensional image with periodic elements, whereby the scan line represents a one-dimensional slice of target shape.
Each slice is broken down into “segments” consisting of sets of adjacent pixels that are similar in brightness, hue, or both. The camera, in conjunction with processing by ancillary electronic data processing means and methods, delivers the slices with segments to the system where they are sequenced. The system identifies every slice and segment to determine what feature of the target shape the slice and segment represent. Thereafter, predefined rules are used to determine if each identified segment deviates from numerical acceptance norms. The method is thus used to analyze periodic elements of any arbitrary target shape, thereby working in a range of different object acceptance norms and being easily adaptable to change from one set of norms to another.
The algorithm used in the inventive method accomplishes at least two outcomes simultaneously. It serves as a framework for the transformation of a set of measurements that could be made on a two-dimensional image of the target area into a set of measurements made only on the one-dimensional data set. In addition, the algorithm is capable of accurately measuring a lattice constant, i.e., the spacing between the geometric elements of an array that arises from a data set only after certain manipulations have been made. The lattice constant can be measured without pr
K-G Devices Corp.
Lee Young
Morgan & Lewis & Bockius, LLP
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