There is provided a technique that includes: a camera configured to capture images of the target object; a memory configured to store the images of the target object and feature data including one or more predetermined exterior features of the target object; and a processor configured to: determine a first process configuration for an operation including a plurality of image processes; perform the operation under the first process configuration; generate inspection data from the sets of images that have been processed; generate an inspection score by comparing the inspection data with the feature data; compare the inspection score with a predetermined threshold score; set the first process configuration as an optimal configuration if the inspection score satisfies the predetermined threshold score.

There is provided a surface abnormality detection device, and a system, capable of detecting an abnormal portion having a displacement below the distance measurement accuracy when detecting the abnormal portion on the surface of a structure. A surface abnormality detection device includes a classification means for classifying an object under measurement into one or more clusters having the same structure, based on position information at a plurality of points on a surface of the object under measurement; a determination means for determining a reflection brightness normal value of the cluster based on a distribution of reflection brightness values at a plurality of points on a surface of the cluster; and an identification means for identifying an abnormal portion on the surface of the cluster based on a difference between the reflection brightness normal value and the reflection brightness value at each of the plurality of points.

An information processing apparatus includes an image obtaining unit configured to obtain an image, a first determining unit configured to determine a first image range to be used in making a determination related to inspection of an inspection target included in the image, based on a detection result of the inspection target from the image, and a second determining unit configured to determine a second image range to be used in recording an inspection result of the inspection target, the second image range being an image range indicating a wider range than a range indicated by the first image range.

A smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab are provided. The intelligent defect diagnosis method includes receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system. The method utilizes the precisely calibrated coordinate, the defect layout pattern, and the higher accurate calibrated defect size value. So, a more precise killer defect index can be generated with calibrated coordinate deviation calibration and defect size deviation calibration. When judging a defect relating to short circuit or open circuit failure probability, the defect failure result is more accurate and less incorrect judgment.

A defect image including a defect and a defect-free image not including a defect for an article different from an inspection article are acquired to teach an identifier when inspecting for a defect in the inspection article. The identifier that has learned the images is made to identify whether an extracted inspection image obtained by segmenting the inspection image of the inspection article includes the defect and the identification results of the identifier are used to determine whether a defect is present in the inspection article. When teaching the identifier the defect, the identifier is provided with, as learning images, a plurality of extracted defect images generated from the defect image by changing an extracting region for extraction from the defect image such that the defect in the defect image is at a different position in each of the plurality of extracted defect images.

A method can include receiving image data characterizing a viewed object acquired via an image sensor of a visual inspection system and providing the image data in a display. The method can include receiving a first directional movement input via a directional input device of the visual inspection system and applying a first set of actuator drive signals to a plurality of actuators of the visual inspection system. The method can further include applying a coordinate transformation to the image data to generate transformed image data and receiving a second directional movement input via the directional input device. The method can also include applying a second set of actuator drive signals to the plurality of actuators. The second set of actuator drive signals can cause points on the viewed object to move in the first direction on the display. Related systems performing the method are also provided.

A method and device for automatically identifying a point of interest in a depth measurement on a viewed object using a video inspection device is disclosed. The video inspect device determines the three-dimensional coordinates in a region of interest on the viewed object and analyzes those surface points to determine the desired measurement application (e.g., determining the deepest point, the highest point, or the clearance between two surfaces). Based on the desired measurement application, the video inspection device automatically identifies the point of interest on the viewed object and places a cursor at that location.

A method is disclosed. The method may include generating a first optical image of a sample with a first inspection sub-system. The first optical image may be generated when a first set of photoluminescent markers are emitting photoluminescent illumination at a first time interval. The method may include generating additional optical images with an additional inspection sub-system. The additional optical images may be generated when additional photoluminescent markers are emitting photoluminescent illumination at additional time intervals. The method may include generating an accumulated optical image based on the first optical image and the additional optical images. The method may include determining a location of the photoluminescent markers based on the accumulated optical image. The method may include determining a pattern of the sample based on the determined location of the photoluminescent markers.

A smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method includes receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system. This method utilizes the precisely calibrated coordinate, the defect layout pattern, and the higher accurate calibrated defect size value. So, a more precise killer defect index can be generated with calibrated coordinate deviation calibration and defect size deviation calibration. When judging a defect relating to short circuit or open circuit failure probability, the defect failure result is more accurate and less incorrect judgment.

A marking region image is obtained by cutting out the part corresponding to a marking region from an article image obtained by imaging an article to be inspected. Then, whether or not the marking is properly provided is determined by performing a character recognition of a marking part for a marking region image. Further, an image of an article having no marking and no defect is stored as a reference image, whereas a marking periphery image obtained by removing the image of the marking part from the marking region image is compared to the reference image. By that comparison, whether or not any defect is included in the marking peripheral part of the marking region except the marking part is determined.