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 deposit detection device according to an embodiment includes a detection module, a determination module, and an identification module. The detection module detects a deposit region corresponding to a deposit adhering to an imaging device, based on brightness information of an image captured by the imaging device. The determination module determines whether variation in brightness information in a predetermined region of the image is within a predetermined range, in a period after a vehicle is stopped in a state in which the deposit region is detected by the detection module. The identification module identifies brightness information serving as a determination criterion of the deposit region when the determination module determines that the variation in brightness information is within a predetermined range.

A detection apparatus that detects a mark formed on a substrate is provided. The detection apparatus comprises a detection optical system that irradiates light on the mark on the substrate held by a stage and detects an image of the mark, and a processor that performs a detection process of the mark based on the image of the mark. The processor finds a detection value indicating a position of the mark in an observation field of the detection optical system based on the image of the mark, finds a subregion in which the mark is located among a plurality of subregions in the observation field, and corrects the detection value based on a correction value corresponding to the found subregion among correction values predetermined for the plurality of subregions, respectively.

The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.

A method of measuring a gas turbine engine component of a gas turbine engine according to an example of the present disclosure includes, among other things, providing at least one gas turbine engine component including a coating on a substrate, detecting infrared radiation emitted from at least one localized region of the coating at a first wavelength in a first electromagnetic radiation frequency range, detecting infrared radiation emitted from the substrate corresponding to the at least one localized region at a second, different wavelength in a second electromagnetic radiation frequency range that differs from the first electromagnetic radiation frequency range, and determining a heat flux relating to the at least one localized region based upon a comparison of the first wavelength and the second wavelength.

Disclosed is a method for estimating twin defect density in a single-crystal sample, including: (A) etching the observed surface of a single crystal to form etch pits; (B) selecting bar-shaped etch pits caused by twin defect; (C) from the long-axis direction lengths of the etch pits caused by twin defect, estimating the twin defect density by using the following equation: twin defect density=Σkx′.sub.i/area of sample, wherein 2≤k≤3, and x′.sub.i is the long-axis direction length of an etch pit caused by the i-th twin.

The present invention relates to 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 comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to 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 comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.