An apparatus for inspecting an appearance of capsule 40 in which cap 42 is fitted to body 41 includes a carrying body that carries capsule 40 housed in housing part 33, contact body 34 that rotates capsule 40 carried inside housing part 33 due to a contact friction, imaging device 17 that takes an image of the capsule rotating, and a determination device that determines if the capsule is good or poor based on an imaging data taken by imaging device 17. Wherein contact body 34 moves an inside of housing part 33 to a side of body 41 by rotating the capsule, which is housed in housing part 33 being separated from other capsules, around an axis direction, and the determination device determines an orientation of capsule 40 based on a position of capsule 40 in housing part 33.

The present invention relates to a device (1) for detecting surface defects in an object (100), for example an industrial gasket. The detection device comprises lighting means (2) configured to illuminate said object with a first light radiation (L.sub.1) having a first lighting direction (D.sub.1) or with a second light radiation (L.sub.2) having a second lighting direction (D.sub.2). According to the invention, the detection device comprises acquisition means (30) configured to acquire a plurality of B/W images of said object, when illuminated by said lighting means.

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.

Systems and methods are described for determining whether liquid remains on a wafer surface following a scanning operation. A system embodiment includes, but is not limited to, a first system configured for positioning adjacent a transfer line coupled with a scanning nozzle to dispense fluid onto a wafer surface and to recover the fluid from the wafer surface, the first system configured to detect a gas/liquid transition of the fluid and determine a volume of liquid sample dispensed; a second system configured for positioning adjacent a second line downstream from the scanning nozzle, the second system configured to detect a gas/liquid transition of fluid flowing through the second line and determine a volume of liquid sample recovered from the wafer surface; and a controller configured to generate an alert if the volume of liquid sample recovered is not within a threshold amount compared to the volume of liquid sample dispensed.

A method includes patterning a hard mask over a target layer, capturing a low resolution image of the hard mask, and enhancing the low resolution image of the hard mask with a first machine learning model to produce an enhanced image of the hard mask. The method further includes analyzing the enhanced image of the hard mask with a second machine learning model to determine whether the target layer has defects.

A method for quality inspection of laser material processing includes performing laser material processing on a workpiece and generating, by a sensor, raw image data of secondary emissions during the laser material processing of the workpiece. The method also includes determining a quality of the laser material processing by analyzing the raw image data of the secondary emissions.

A method for quality inspection of laser material processing includes performing laser material processing on a workpiece and generating, by a sensor, raw image data of secondary emissions during the laser material processing of the workpiece. The method also includes determining a quality of the laser material processing by analyzing the raw image data of the secondary emissions.

Systems and methods for automatically grading a user device are provided. Such systems and methods can include (1) a lighting element positioned at an angle relative to a platform, (2) an imaging device positioned at the angle relative to the platform such that light emitted from the lighting element and a field of view of the imaging device form a right angle where the light emitted from the lighting element and the field of view meet at a user device when the user device is positioned at a predetermined location on the platform, and (3) control circuitry that can activate the lighting element, instruct the imaging device to capture an image of a screen of the user device while the user device is at the predetermined location and is being illuminated by the first lighting element, and parse the image to determine whether the screen is damaged.

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 computer-implemented method comprising: receiving data comprising two-dimensional data and three-dimensional data of a component of an engine; identifying a feature of the component using the two-dimensional data; determining coordinates of the feature in the two-dimensional data; determining coordinates of the feature in the three-dimensional data using: the determined coordinates of the feature in the two-dimensional data; and a pre-determined transformation between coordinates in two-dimensional data and coordinates in three-dimensional data; and measuring a parameter of the feature of the component using the determined coordinates of the feature in the three-dimensional data.