A method for processing semiconductor wafers includes obtaining measurement data from a surface of a semiconductor wafer processed by a front-end process tool. The method includes determining a center plane of the wafer based on the measurement data, generating raw shape profiles, and generating ideal shape profiles. The method further includes generating Gapi profiles based on the raw shape profiles and the ideal shape profiles, and calculating a Gapi value of the semiconductor wafer based on the Gapi profiles. The generated Gapi profiles and/or the calculated Gapi value may be used to tune the front-end process tool and/or sort the semiconductor wafer for polishing. Systems include at least a front-end process tool, a flatness measurement tool, and a computing device.

The present invention enables highly accurate analysis when visualizing analysis results in spectral imaging.

An surface analysis method includes: acquiring spectral image data regarding a sample surface with use of a spectral camera; extracting n wavelengths dispersed in a specific wavelength range in the acquired spectral image data, and converting spectrums of the wavelengths in the spectral image data into n-dimensional spatial vectors for each pixel; normalizing the spatial vectors of the pixels; clustering the normalized spatial vectors into a specific number of classifications; and identifying and displaying pixels clustered into the classifications, for each of the classifications.

In a crack detection device (10), an image acquisition unit (21) acquires image data acquired by taking an image of a road surface from an oblique direction with respect to the road surface, An image classification unit (22) classifies image data acquired into an acceptable range with a resolution higher than a standard value, and an unacceptable range with a resolution equal to or less than the standard value. A data output unit (23) outputs acceptable data being image data of a part classified into the acceptable range as data to detect a crack on the road surface. An image display unit (24) displays data output.

A method for processing semiconductor wafers includes obtaining measurement data of an edge profile of a semiconductor wafer processed by a front-end process tool. The method includes determining an edge profile center point based on the measurement data, generating a raw height profile, and generating an ideal edge profile. The method further includes generating a Gapi edge profile of the semiconductor wafer based on the raw height profile and the ideal edge profile and calculating a Gapi edge value of the semiconductor wafer based on the Gapi edge profile. The generated Gapi edge profile and/or the calculated Gapi edge value may be used to tune the front-end process tool and/or sort the semiconductor wafer for polishing. Systems include at least a front-end process tool, a flatness measurement tool, and a computing device.

There is described a fixture for supporting a plurality of gas turbine engine blades that are to be imaged. The mobile fixture comprises a plurality of interconnected mounts arranged to lie within a common plane, wherein each mount comprises at least one support surface for holding a respective blade. Support surfaces of respective mounts are oriented such that their respective normal vectors have the same angle with respect to the common plane.

A thickness estimating apparatus includes a transfer robot, a light source, a camera, a memory and a controller. The memory stores a thickness predicting model generated based on a data set including a thickness of at least one of a test wafer corresponding to the wafer or a test element layer formed on the test wafer, and the thickness predicting model being trained to minimize a loss function of the data set. The controller applies pixel data, which is acquired from at least one pixel selected from a plurality of pixels included in a captured image, to the thickness predicting model, to predict a thickness of at least one of the wafer or an element layer formed on the wafer in a position corresponding to a position of the selected pixel.

A system for inspecting thin glass includes: a housing including a body and a cover; a first shuttle which fixes an edge portion of the thin glass and reciprocates in a first axis direction; a first inspection part disposed on the body and which measures a position of a defect formed in the thin glass by taking a picture of the thin glass; a transport shuttle which separates the thin glass from the first shuttle, a second shuttle which separates the thin glass from the transport shuttle, fixes the thin glass, and reciprocates the upper surface of the body; and a second inspection part disposed on the body and spaced apart from the first inspection part and which inspects the position of the defect by taking an enlarged picture of the position of the defect. The first shuttle tensions and fixes the thin glass.

In a processing apparatus, a vibration detecting unit includes a light source, an interference unit configured to apply light emitted from the light source to a measurement target member and generate an interference pattern image. A control unit includes a storage section configured to store a first interference pattern image captured at a predetermined timing by the imaging unit and a second interference pattern image captured at a timing different from the timing of the first interference pattern image by the imaging unit, a comparing section configured to compare the first interference pattern image and the second interference pattern image stored in the storage section with each other, and a vibration detecting section configured to detect vibration on the basis of the first interference pattern image and the second interference pattern image compared with each other by the comparing section.

Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.

A reticle inspection system may include two or more inspection tools to generate two or more sets of inspection images for characterizing a reticle, where the two or more inspection tools include at least one reticle inspection tool providing inspection images of the reticle. The reticle inspection system may further include a controller to correlate data from the two or more sets of inspection images to positions on the reticle, detect one or more defects of interest on the reticle with the correlated data as inputs to a multi-input defect detection model, and output defect data associated with the defects of interest.