A debris detection apparatus and a debris detection method, capable of easily detecting a metal debris existing on the surface of an inspection target by emitting electromagnetic waves having a wavelength in a far-infrared band toward the inspection target during a battery manufacturing process and then analyzing the characteristics of reflected waves from the surface of the inspection target through a thermal image recorder.

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. 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 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. 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 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.

Stack fault inspection apparatus and method are disclosed. The apparatus includes a sample stage fixing the silicon carbide substrate and allow the incident light to scan the substrate surface; an incident light source configured to irradiate a vertical illumination light of a wavelength corresponding to an energy greater than a band gap energy of the substrate to at least a portion of a surface of the substrate in a direction substantially perpendicular to the surface of the substrate; a photomultiplier tube (PMT) configured to obtain a photoluminescence mapping image having a wavelength corresponding to the band gap energy of the substrate from the surface of the substrate; and a controller configured to process the mapping image and identify stacking faults.

The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.

A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range, and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.

A defect detecting device includes an illumination that irradiates a measuring object with illumination light, an imager that images the illumination light reflected by the measuring object, and a detector that detects a defect at a surface of the measuring object based on a captured image obtained by imaging the illumination light by the imager. The captured image includes a plurality of spectral images having different spectral wavelengths, and the detector detects a diffuse reflection region by which the illumination light is diffusely reflected based on the plurality of spectral images, and determines a size of the defect based on the spectral wavelength of the spectral image in which the diffuse reflection region is detected.

A method includes receiving, by a processing device, a plurality of images of a test object, the plurality of images including a plurality of surfaces of the test object. The processing device selects a region of interest in each of the plurality of images, the region of interest comprising the test object having a background removed. For the plurality of regions of interest as selected, the method includes comparing, by the processing device, each region of interest with a corresponding profile image and identifying defects in each region of interest. The method includes grading, by the processing device, a cosmetic appearance of each region of interest based on the identified defects. The grades of the cosmetic appearance for each region of interest are stored.

A system and method for grading the cosmetic appearance of a test object based on multi-region determination of cosmetic defects are disclosed. The method may include grading a test object, such as, but not limited to, a computing device, for refurbishing and selling refurbished devices. In at least one embodiment, the method may include receiving, by a processing device, a plurality of images of surfaces of a test object. A region of interest in each of the plurality of images is identified and defects are identified. The cosmetic appearance of each region of interest is graded based on the identified defects and are stored. The system may include a housing with a translucent dome and a movable platform for receiving and positioning a computing device. The system may include a plurality of cameras and lights arranged to capture different views of the computing device.