Cognex Corporation

Other

Joseph R Scola

Sanjay J Nichani

Lowell D Jacobson

David Y Li

Paul Dutta-Choudhury

Vladimir N Ruzhitsky

Bradford Safford

A system and method for object inspection incorporates a level of hierarchy so that a single, primary alignment may be performed to generate a pose for multiple inspection regions. As a result, a Training mode may include the specification of a single alignment model window and a list of associated inspection region windows. Similarly, a Run-time Inspection Mode may also include specification of a single alignment model window and a list of associated inspection region windows. Such an implementation addresses the long standing problems associated with performing one or more types of inspection operations in one or more regions of a given sample-object image. By virtue of the hierarchical structure, operation of multiple inspection tools may be performed simultaneously or in any order and inspection of multiple inspection regions may be performed simultaneously or in any order.

System and method for bundled location and regional inspection

A method and apparatus are disclosed for analyzing a boundary of an object. An embodiment for determining defects of a boundary to sub-pixel precision and an embodiment for fast correlation scoring are disclosed. The boundary is analyzed by matching a first boundary, such as a model of an ideal object boundary, to a second boundary, such as the boundary of an object being produced at a factory. The boundaries are represented as a set of indexed vertices, which are generated by parsing the boundaries into a set of segments. One embodiment refines the parse through merging segments and reassigning data points near the endpoints of the segments. The model produced is very accurate and is useful in other applications. To analyze the boundaries, the sets of indexed vertices are matched, and optionally the segmentation of the second boundary is refined to increase the extent of matching. The extent of matching yields a correlation score and/or the matching allows a characteristic class of the first set of segments to be associated with the second set of segments. The class and data points of the runtime segments are used to measure defects in the second set of segments, and thus in the boundary of the object.

Boundary analyzer

Machine vision methods for inspection of semiconductor die lead frames include the steps of generating a first image of the lead frame, generating a second image of the lead frame and any defect thereon, and subtracting the second image from the first image. The methods are characterized in that the second image is generated such that subtraction of it from the first image emphasizes the defect with respect to the lead frame.

Semiconductor device image inspection with contrast enhancement

Provided is the ability to validate detected features in acquired images to thereby enhance the integrity of any analysis carried out on the detected and validated features. A sequence of images of, e.g., an object is acquired, each image in the sequence corresponding to a distinct orientation of the object about a selected object axis. Images in the sequence are inspected for feature points of the selected feature plane, as-projected into the images, at a first feature detection location and at a second feature detection location. The second feature detection location is configured at an image position at which a feature point detected in the first feature detection location in a first inspected image is expected to appear in a second inspected image. Valid object feature points are identified as being those feature points which are detected in both the first feature detection location in a first inspected image and in the second feature detection location in a second inspected image of the image sequence. Features that are validated are not likely to be time-dependent noise and are preserved for further feature analysis, while extraneous data is rendered transparent to feature analysis. Further, an object feature plane can be analyzed for a specified feature configuration even when only a subset of feature points is available in any one given image of the object, e.g., where a portion of a three-dimensional object obscures other portions of the object in a selected view of the object. Also provided is a method for detecting features of a semiconductor melt surface as a semiconductor ingot is pulled out of the melt.

Machine vision system for object feature analysis and validation based on multiple object images

A machine vision method is provided for segmenting an image based on an adaptive thresholding approach that exploits prior knowledge of the characteristics, such as area, of the object to be segmented. Adaptation to varying gray-values of the candidate object/background from scene-to-scene is obtained by using a sampling window which provides gray-value statistics of the candidate object to be segmented. Next, using these statistics, a characteristic of the image of the object to be segmented, such as its area or its gray-value standard deviation, and the histogram of the image, the segmentation is performed. The invention is especially useful for detecting the presence/absence of adhesive on printed circuit boards, where both the appearance of the printed circuit board and the solder paste commonly found thereon make segmentation difficult. In case of presence/absence determination, an additional step of shape matching is employed.

Model-based adaptive segmentation

Machine vision methods for inspection of semiconductor die surfaces include the steps of generating a first image of the die surface, generating a second image of the die surface and any defect thereon, and subtracting the second image from the first image. The methods are characterized in that the second image is generated such that subtraction of it from the first image emphasizes the defect with respect to the die surface.

Semiconductor device image inspection utilizing image subtraction and

A method and apparatus for identifying an object having an identifying feature includes a two step process for detecting the identifying feature. The process involves locating the feature based upon a first characteristic of the feature and then verifying that feature is the identifying feature based upon a second characteristic of the feature. Preferably, the first step of the process is less discriminating and substantially faster than the second step. This permits the process to be performed rapidly without reducing accuracy.

Growing community of inventors

Medfield, MA, United States of America

Joseph R Scola