A system and a method for assessing reliability of an electronic component. The method may include training a machine earning (ML) algorithm and/or a classification network to classify electronic components based on one or more features, attributes or characteristics related to reliability of the electronic components, e.g., related to a level of solderability of the components lead or balls or features indicating of tampering of the electronic component. By receiving an image of a test electronic component and extracting a feature related to reliability of the test electronic component from the image received, embodiments of the invention may enable classifying the test electronic component to a class indicating a reliability of the test electronic component by using the machine learning algorithm and/or the classification network.

A system, device and method for deployment of one or more dust accumulation sensors receives a baseline measurement associated with no accumulation of dust in a target environment, receives a time-elapsed measurement associated with positive accumulation of dust in the target environment, determines a quantity of accumulated dust in the target environment based on the baseline measurement and the time-elapsed measurement, generates a spatial dust deposition distribution for the target environment based on the determined quantity of accumulated dust and determines a deployment for one or more dust accumulation sensors for the target environment based on the spatial dust deposition distribution.

A method of testing a display device includes obtaining a photographed image by photographing a target substrate, where the target substrate includes patterns arranged in a first direction and a second direction, obtaining grayscale values of the patterns by grayscaling the photographed image, determining an inspection target pattern from among the patterns, obtaining a first comparison value by comparing a grayscale value of the inspection target pattern with a grayscale value of a first vertically adjacent pattern adjacent in the first direction, obtaining a second comparison value by comparing the grayscale value of the inspection target pattern with a grayscale value of a first diagonally adjacent pattern adjacent in a third direction crossing the first and second directions, obtaining a compensated comparison value by compensating the first comparison value based on the second comparison value, and determining a defect of the inspection target pattern based on the compensated comparison value.

A non-transitory computer-readable recording medium recording an image processing program that causes a computer to execute processing of: specifying a damaged portion by analyzing a captured image of a construction; and predicting, in the captured image, a range to which damage spreads based on the specified damaged portion and design data associated with the construction.

A method for quantitatively measuring internal defects in an as-cast steel product includes optically scanning at least a portion of a surface of the steel product with a scanning device to create a digital image thereof, analyzing the digital image to calculate a quantitative value for an amount of internal defects therein, and normalizing the quantitative value to a rating according to a standardized scale.

In a real-time traceability method of a width of a defect based on divide-and-conquer provided by the present invention, through the calibration transfer function, the multidimensional eigenvector analysis technology based on the electromagnetic field simulation database of defect scattered dark-field imaging and the adaptive threshold segmentation method, the real-time traceability of the width of the defect greater than and close to the diffraction limit of the system is performed, respectively. The extreme random tree regression model is trained by multidimensional eigenvector analysis technology based on the multidimensional eigenvectors in the electromagnetic field simulation database of the defect scattered dark-field imaging. The present invention solves the problems that the width of the defect in defect detection is difficult to be accurately measured in real time, and the conventional image processing algorithm is difficult to accurately identify the width of the defect close to the diffraction limit of the system.

A device may detect a semiconductor wafer to be transferred from a source wafer carrier to a target wafer carrier, and may cause a light source to illuminate the semiconductor wafer. The device may cause a camera to capture images of the semiconductor wafer after the light source illuminates the semiconductor wafer, and may perform image recognition of the images of the semiconductor wafer to determine whether an edge of the semiconductor wafer is damaged. The device may cause the semiconductor wafer to be provided to the source wafer carrier when the edge of the semiconductor wafer is determined to be damaged, and may cause the semiconductor wafer to be provided to the target wafer carrier when the edge of the semiconductor wafer is determined to be undamaged.

An apparatus and method for coating an electrode slurry are disclosed herein. In some embodiments, the apparatus includes a coater configured to coat an electrode slurry on a metal foil, a measuring unit is configured to measure a remaining oil level on a surface of the metal foil before the coater coating the electrode slurry, and a controller configured to control the coater to coat the electrode slurry based on a measurement value of the remaining oil level of the surface of the metal foil.

A method detects mechanical equipment parts. The method includes: obtaining an image of a part; extracting a feature from the image using a machine learning model, identifying a type of surface defect on the basis of the feature to obtain an identification result; and determining whether to replace the part on the basis of the identification result and a predetermined standard of the part. The method reduces the difficulty of detecting a part, can accurately identify a surface defect of the part and determine whether the part needs to be replaced, thereby improving the work efficiency, and shortens the time for mechanical equipment to stop operating for maintenance, thus improving the operating efficiency of the mechanical equipment. The method is automatically executed by a computer, thereby avoiding manually checking errors, improving the accuracy of detection results, and thus improving the reliability of operation of the mechanical equipment.

A defect inspection system is disclosed, and comprises a linear light source, N number of cameras, a display device, a tag reader, and a modular electronic device, in which the linear light source, the cameras and the modular electronic device are used for conducting a defect inspection of an article. On the other hand, the display device, the tag reader and the modular electronic device are adopted for conducting in production of at least one labeled example. Therefore, the modular electronic device is allowed to apply a machine learning process to an image classifier under using a training dataset containing the labeled examples, thereby producing at least one new defect recognition model or updating the existing defect recognition model.