Mitutoyo Corporation

Other

Shannon Roy Campbell

Robert Kamil Bryll

William Todd Watson

Mark Lawrence Delaney

Lukasz Redlarski

Tracy L Campbell

A metrology system is provided including a projected pattern for points-from-focus type processes. The metrology system includes an objective lens portion, a light source, a pattern projection portion and a camera. Different lenses (e.g., objective lenses) having different magnifications and cutoff frequencies may be utilized in the system. The pattern projection portion includes a pattern component with a pattern. At least a majority of the area of the pattern includes pattern portions that are not recurring at regular intervals across the pattern (e.g., as corresponding to a diverse spectrum of spatial frequencies that result in a relatively flat power spectrum over a desired range and with which different lenses with different cutoff frequencies may be utilized). The pattern is projected on a workpiece surface (e.g., for producing contrast) and an image stack is acquired, from which focus curve data is determined that indicates 3 dimensional positions of workpiece surface points.

Metrology system with projected pattern for points-from-focus type processes

A workpiece inspection and defect detection system includes a light source, a lens that inputs image light arising from a surface of a workpiece, and a camera that receives imaging light transmitted along an imaging optical path. The system utilizes images of workpieces acquired with the camera as training images to train a defect detection portion to detect defect images that include workpieces with defects. Anomaly detector classification characteristics are determined based on features of the training images. Run mode images of workpieces are acquired with the camera, and based on determined features from the images, the anomaly detector classification characteristics are utilized to determine if the images of the workpieces are classified as anomalous. In addition, the defect detection portion determines if images are defect images that include workpieces with defects and for which additional operations may be performed (e.g., metrology operations for measuring dimensions of the defects, etc.)

Workpiece inspection and defect detection system including monitoring of workpiece images

A workpiece inspection and defect detection system includes a light source, a lens that inputs image light arising from a surface of a workpiece, and a camera that receives imaging light transmitted along an imaging optical path. The system utilizes images of workpieces acquired with the camera as training images to train a defect detection portion to detect defect images that include workpieces with defects, and determines a performance of the defect detection portion as trained with the training images. Based on the performance of the defect detection portion, an indication is provided as to whether additional defect images should be provided for training. After training is complete, the camera is utilized to acquire new images of workpieces which are analyzed to determine defect images that include workpieces with defects, and for which additional operations may be performed (e.g., metrology operations for measuring dimensions of the defects, etc.)

Workpiece inspection and defect detection system indicating number of defect images for training

A method of automatically adjusting lighting conditions improves the results of points from focus (PFF) 3D reconstruction. Multiple lighting levels are automatically found based on brightness criteria and an image stack is taken at each lighting level. In some embodiments, the number of light levels and their respective light settings may be determined based on trial exposure images acquired at a single global focus height which is a best height for an entire region of interest, rather than the best focus height for just the darkest or brightest image pixels in a region of interest. The results of 3D reconstruction at each selected light level are combined using a Z-height quality metric. In one embodiment, the PFF data point Z-height value that is to be associated with an X-Y location is selected based on that PFF data point having the best corresponding Z-height quality metric value at that X-Y location.

Points from focus operations using multiple light settings in a machine vision system

A machine vision inspection system acquires a plurality of images of a workpiece region of interest at various focus heights, and determines a Z-height (e.g., the best focus height) for the region of interest based on a focus peak determining data set for the region of interest. The focus peak determining data set is derived from the plurality of images. The machine vision inspection system also determines Z-height quality meta-data based on data derived from the plurality of images (e.g., based on the focus peak determining data set), and associates the Z-height quality meta-data with the corresponding Z-heights. The Z-height quality meta-data are usable to establish weighting factors that are used in association with the corresponding best focus Z-heights in subsequent operations that fit a workpiece surface representation to a plurality of the best focus Z-heights.

Weighting surface fit points based on focus peak uncertainty

A method for correcting coaxial light image edge location errors in a precision machine vision inspection system is disclosed. The method comprises comparing an edge position measurement of a workpiece edge feature using coaxial light and stage light. Edge position measurements using stage light have a lower uncertainty than that of coaxial light. Position correction factors may be determined from the difference between the two edge position measurements. The position correction factors may be stored for correcting subsequent edge position measurements that are based on images acquired using coaxial light. In some embodiments, position correction factors may be determined based on comparing edge position measurements for a plurality of edges.

Edge location measurement correction for coaxial light images

A refined autofocus method provides optimized lighting between iterative autofocus operations, to reliably provide the best possible autofocus Z-height precision. The method includes a quantitative initial focus Z-height determination based on initial focus curve data from initial autofocus images acquired using initial light control parameters. Then, the camera is set at that initial focus Z-height such that well focused images are provided. Refined (optimized) light control parameters are then determined based on at least one respective image acquired using respective light control parameters at that Z-height, such that an image acquired using the refined light control parameters provides a near-optimum value for a contrast-related metric (e.g., a focus metric) at that Z-height. Then, refined autofocus images are acquired using the refined light control parameters and a refined precise Z-height is quantitatively determined base on the resulting focus curve.

Autofocus video tool and method for precise dimensional inspection

The lawn sprinkler system utilizes a single impulse type sprinkler head and at least one (preferably a series of) ground anchor receptacle(s) for the single sprinkler head. The ground anchors are permanently placed as desired, with the single sprinkler head and water supply hose being moved from one ground anchor to another to complete the watering task. Each ground anchor is provided with indicators to indicate the stop settings for the impulse sprinkler head when installed at that location. The user installs the sprinkler head with its attached hose in the selected ground anchor, and sets the sprinkler head stops in accordance with the indicators on the ground anchor. When the watering has been accomplished at that location, the water is turned off, the sprinkler head moved to another anchor, its stops are adjusted, and the water turned on to continue the process.

Lawn sprinkler system

I present a method for matching the spatial relationships between an input set of feature points and a template set of feature points. A feature point consists of a location in space and a label describing the feature at that location in space. A tessellation over the feature point locations is performed. Next, a search identifies polyhedra that have similar contents, the contents being the angles and labels associated with feature points of the polyhedra. Once a match is found, then appropriate adjacent and neighboring polyhedra are examined. Matching the node labels and angular relationships for a set of appropriate adjacent and neighboring polyhedra extends the volume over which matches exist and significantly increases the certainty that a positive match exists. Because this matching method is based on similarities in node angles and labels, it is independent of transformations in position, rotation, and scale if the angles in the polyhedra created by the tessellation are position, rotation, and scale invariant.

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Woodinville, WA, United States of America

Shannon Roy Campbell