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.

A method for processing semiconductor wafers includes obtaining measurement data of an edge profile of a semiconductor wafer processed by a front-end process tool. The method includes determining an edge profile center point based on the measurement data, generating a raw height profile, and generating an ideal edge profile. The method further includes generating a Gapi edge profile of the semiconductor wafer based on the raw height profile and the ideal edge profile and calculating a Gapi edge value of the semiconductor wafer based on the Gapi edge profile. The generated Gapi edge profile and/or the calculated Gapi edge 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.

A first imaging unit 71 generates a first image a first image by taking an object to be inspected. A guide display unit 72 determines the object to be inspected from the first image by using a model for determining an object to be inspected from an image, and displays an illustration representing the object to be inspected as a guide. A second imaging unit 73 generates a second image by superimposing on the guide, and taking the object to be inspected with a recognizable marker regardless of color of an appearance of an object to be inspected, attached in a vicinity of a defect. A defect position determination unit 74 determines a position of the defect included in the object to be inspected based on a positional relationship between the illustration and the marker included in the second image. An information collecting unit 75 collects defect information associated with a type of the object to be inspected and the position of the defect.

An information processing apparatus selects, as a reference defect, at least one defect from among first defects associated with a first image and selects, as a correction target defect, at least one defect from among second defects associated with a second image captured at a time different from an image capturing time of the first image. Additionally, the information processing apparatus generates a correction candidate by modifying the correction target defect, acquires a matching level representing a matching relationship between the reference defect and the correction candidate, and generates a corrected defect by correcting the correction target defect based on the matching level. Then, the information processing apparatus acquires a progress level representing a change in defect from the reference defect based on a comparison between the reference defect and the corrected defect.

A system for taking high-resolution photographs from a vehicle-mounted camera, forming orthomosaics from video and/or multiple high-resolution photographs, and using artificial intelligence to detect and classify pavement flaws and defects in the imagery. Detection also includes the ability to capture quantifiable metrics for the defects and/or a region of interest. Three-dimensional imagery is produced from the same images as the orthomosaics. Surface and terrain map products made from the same source images capture additional details such as depth and volume. The highlighted orthomosaics and three-dimensional imagery can then be used as a basis to determine the pavement surface condition and subsequently support maintenance orders and manage pavement repairs. Further, metadata such as latitude, longitude, and altitude geo-location coordinates and sampling time can also be transferred to the output products to create a digital time history and enable analysis for preventative maintenance planning. Alternatively-sourced imagery may also be analyzed.

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.

The present invention relates to defects inspection on a silicon carbide wafer or an epitaxial layer thereon to determine the location, and adjustment of the location of the scribe line, which is a separation line forming a gap between adjacent dies. The present invention can obtain high efficiency and economy in the semiconductor process using wafers containing various defects in the surface and thin film, by minimizing the effect of wafer defects on the final yield of the semiconductor chip or die, via adjustment of scribe line positions arranged on the wafer.

A marking region image is obtained by cutting out the part corresponding to a marking region from an article image obtained by imaging an article to be inspected. Then, whether or not the marking is properly provided is determined by performing a character recognition of a marking part for a marking region image. Further, an image of an article having no marking and no defect is stored as a reference image, whereas a marking periphery image obtained by removing the image of the marking part from the marking region image is compared to the reference image. By that comparison, whether or not any defect is included in the marking peripheral part of the marking region except the marking part is determined.

Provided are a defect observation method and a defect observation device which detect a defect from an image obtained by imaging the defect on a sample with an optical microscope by using positional information of the defect on the sample detected by a different inspection device to correct the positional information of the defect and observe in detail the defect on the sample with a scanning electron microscope using the corrected positional information. The defect observation method includes detecting the defect from the image to correct the positional information of the defect, switching a spatially-distributed optical element of a detection optical system of the optical microscope according to the defect to be detected, and changing an image acquisition condition for acquiring the image and an image processing condition for detecting the defect from the image according to a type of the switched spatially-distributed optical element.

A defect inspection device is provided with an illumination optical system that irradiates light or an electron beam onto a sample, a detector that detects a signal obtained from the sample through the irradiation of the light or electron beam, a defect detection unit that detects a defect candidate on the sample through the comparison of a signal output by the detector and a prescribed threshold, and a display unit that displays a setting screen for setting the threshold. The setting screen is a two-dimensional distribution map that represents the distribution of the defect candidates in a three dimensional feature space having three features as the axes thereof and includes the axes of the three features and the threshold, which is represented in one dimension.