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 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.

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.

An optical device includes: a substrate having a first surface, and a second surface opposite of the first surface; a plurality of surface emitting laser elements provided on the first surface of the substrate and configured to emit light in a direction intersecting the first surface; a plurality of optical elements disposed on the second surface so as to respectively correspond to the plurality of surface emitting laser elements; and an anti-reflection structure between the substrate and the plurality of optical elements.

An optical device includes: a substrate having a first surface, and a second surface opposite of the first surface; a plurality of surface emitting laser elements provided on the first surface of the substrate and configured to emit light in a direction intersecting the first surface; a plurality of optical elements disposed on the second surface so as to respectively correspond to the plurality of surface emitting laser elements; and an anti-reflection structure between the substrate and the plurality of optical elements.

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 filter unit for a Raman microscope mounted with a dark-field objective lens unit includes a frame body, a plurality of UV-LED elements that is disposed around a window part of the frame body to emit UV light, and a long-pass filter that is supported to the frame body to cover the window part of the frame body and transmits a light having a wavelength longer than the wavelength of the UV light. The filter unit has a dark-field UV irradiation function, and is able to impart a fluorescence observation function to the Raman microscope.

A method for analysing the quality of a weld bead in a welding zone using a thermal camera. A thermal image (IMG) of a given area is divided into a plurality of sub-areas each having a respective temperature (Ti). During a learning step, the temperature evolution (Ti(t)) of each sub-area is monitored for different welding conditions. During a training step, the temperature evolutions (Ti(t)) are processed for training a classifier (304). For this purpose, a respective cooling curve is extracted (302) from each temperature evolution (Ti(t)), and parameters (F) are determined that identify the shape of each cooling curve. The parameters (F) are used as input features for the classifier (304). In normal operation the temperature evolution (Ti(t)) of each sub-area (Ai) is monitored and the classifier (304) estimates weld quality (S).

An apparatus for detecting a surface condition of an object. The apparatus comprises a light source, a reflective face and an imaging device. The imaging device is configured to receive reflected light emanating from the reflective face. The reflective face is oriented at a first acute angle relative to an optical axis of the imaging device, such that a projection area of a virtual image of a surface formed via the reflective face is greater than a projection area of the surface on a plane perpendicular to the optical axis.