Abstract: In learning processing performed before sample observation processing (steps S705 to S708), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S702), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S703), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S704).

Abstract: Disclosed is an operating method of an electronic device for manufacture of a semiconductor device. The method includes receiving, at the electronic device, a computer-aided design (CAD) image for a lithography process of the semiconductor device, and generating, at the electronic device, a first scanning electron microscope (SEM) image and a first segment (SEG) image from the CAD image by using a machine learning-based module, and the first SEG image includes information about a location of a defect.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a semantic segmentation model configured for assigning labels to each of multiple pixels in an image responsive to what is represented in each of the multiple pixels. The image is an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the assigned labels and without a reference image for the specimen.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: Methods and systems for detecting defects on a specimen are provided. One system computes different candidate reference images from different combinations of images of the specimen generated by an inspection subsystem and combines different portions of the candidate reference images without modification to thereby generate a final reference image. The final reference image is then used for defect detection, which may be single or double detection. The embodiments are particularly useful for defect detection in areas of specimens including only non-resolvable, repeating device patterns, like cell regions, but may be used for inspection of other types of areas as well.

Abstract: A registration means for storing an image of a product as a registration image in association with information representing the passing sequence that the product passed through an upstream side process; a management means for managing the matching sequence in a downstream side process; and a matching means for performing matching between an image of a product carried into the downstream side process and the registration image according to the matching sequence, are included. Each time the matching means succeeds in matching, the management means updates the matching sequence to sequence in which registration images not having succeeded in matching with any matching image are put in order on the basis of the passing sequence that the products passed through the upstream side process.

Abstract: Described herein are various techniques for managing media related to a construction project, which may relate to one or more buildings to be built and/or renovated as part of the construction project. In accordance with techniques described herein, the media may be managed according to information regarding the construction project that is stored by one or more other systems separate from a media management system. The other systems may be construction information management systems that each store information regarding a construction project, including information relating to planning and/or execution of the construction project. For example, the media management system may generate tags based on the information regarding the construction project, may be trained to recognize features in the media that relate to the tags, and add the appropriate tags to the media based on the recognized features.

Abstract: A defect inspecting system includes a detector configured to image a sample and a host control device that acquires an inspection image including a defect and a plurality of reference images not including a defect site and generates a pseudo defect image by editing a predetermined reference image among the plurality of acquired reference images. An initial parameter is determined with which the pseudo defect site is detectable from the pseudo defect image. The host control device acquires a defect candidate site from the inspection image using the initial parameter, estimates a high-quality image from an image of a site corresponding to the defect candidate site using the parameter acquired in image quality enhancement, and specifies an actual defect site in the inspection image by executing defect discrimination. A parameter is determined with which a site close to the specified actual defect site is detectable using the inspection image.

Abstract: Provided are an image defect detection method and apparatus, an electronic device, a storage medium and a product. The method includes acquiring a to-be-detected image; obtaining a restored image corresponding to the to-be-detected image based on the to-be-detected image, at least one mask image group and a plurality of defect-free positive sample images, where each mask image group includes at least two binary images having a complementary relationship, and different mask image groups have different image sizes; and locating a defect of the to-be-detected image based on the to-be-detected image and each restored image. The solution solves the problem in which a related defect detection method requires numerous manual operations and has a low detection accuracy due to subjective factors of a worker.

Abstract: A method, apparatus, and device for labeling images of PCBs includes obtaining an image to be tested; comparing the image to be tested to a reference image to generate an image mask, the image mask includes several connected domains; detecting defects of the image to be tested; when at least one defect is detected in the image to be tested, obtaining a coordinate of the at least one defect; based on a central coordinate of the connected domains and the coordinate of the at least one defect, determining the connected domains to be defect connected domains or normal connected domains; generating a first image mask and a second image mask; and processing the first image mask and the second image mask with the image to be tested to obtain a defect element image corresponding to the defect connected domains and a normal element image corresponding to the normal connected domains.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes an output acquisition subsystem configured to generate output for a specimen at one or more target locations on the specimen and one or more temperature sensors configured to measure one or more temperatures within the system. The system also includes a deep learning model configured for predicting error in at least one of the one or more target locations based on at least one of the one or more measured temperatures input to the deep learning model by the computer subsystem. The computer subsystem is configured for determining a corrected target location for the at least one of the one or more target locations by applying the predicted error to the at least one of the one or more target locations.

Abstract: Disclosed herein are methods and systems of metadata management for reviewing data from microscopy experimental sessions. Image data from an experimental session is stored in an archive at one or more filepath locations, either locally or on a network. Metadata associated with the image data is stored in a database with a reference to the filepath where the raw image is stored, such that the metadata is associated in the database with the image data. A user can perform post-experimental filtering, sorting, and searching of the underlying image data using the metadata, which allows the image data to be analyzed without duplication of the image data and without manual review of each individual image. The filtered data is presented in an interactive timeline format.

Abstract: Various embodiments may involve obtaining an image of at least a section of a manufactured part; determining, based on executing a neural network on the image, that the manufactured part was not fabricated according to a specification for the manufactured part, wherein the neural network was trained to associate images of manufactured parts with corresponding indicators of specifications for the manufactured parts; and, in response to determining that the manufactured part was not fabricated according to the specification, generating an electronic alert indicating that the manufactured part was improperly fabricated.

Abstract: An inspection system includes machine learning circuitry configured to determine whether each of objects belongs to a predetermined attribute based on feature data of each of the objects, feature data acquisition circuitry configured to acquire feature data of reevaluated objects which are determined to belong to the predetermined attribute without using the machine learning circuitry among excluded objects which are determined not to belong to the predetermined attribute by the machine learning circuitry, and parameter update circuitry configured to update a learning parameter of the machine learning circuitry based on teaching data including the acquired feature data acquired by the feature data acquisition circuitry.

Abstract: An image acquisition method includes storing a coefficient of a relational expression between a parameter corresponding to a light quantity incident on an imaging sensor including a photo sensor element and an output value of the imaging sensor in the case of the light incident on the imaging sensor which employs a reference image accumulation time, inputting a desired image accumulation time, and calculating a parameter for obtaining a desired output value of the imaging sensor by using a corrected relational expression obtained by correcting using an output value of the imaging sensor employing the desired image accumulation time in the case of the incident light quantity being zero, adjusting the light quantity incident on the imaging sensor to be a calculated parameter, and acquiring a target image by the imaging sensor on which an adjusted light quantity is incident, and outputting data of the acquired image.

Abstract: 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.

Abstract: A defect characterization method includes: a first scanning image and target defect coordinates in the first scanning image are obtained; a first defect image is obtained according to the target defect coordinates in the first scanning image, the first defect image containing a defect area where a target defect is located and a noise area not containing the target defect; the noise area is marked, Automatic Defect Review (ADR) calculation is performed on the defect area, and a pixel level value of a defect in the defect area is obtained; coordinates of the defect with a maximum pixel level value are obtained, and a second defect image is obtained according to the coordinates of the defect with the maximum pixel level value; and the defect with the maximum pixel level value is classified according to the second defect image.

Abstract: A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.

Abstract: A keyboard file verification method based on image processing comprises controlling a processor to perform following operations: obtaining a keyboard file; generating, according to the keyboard file, a search index and a feature image; obtaining a template image from a template database according to the search index; performing a calibration operation according to the feature image, wherein the calibration operation comprises: adjusting a resolution of the feature image according to a resolution of the template image; performing a shifting operation according to the feature image, to generate a plurality of candidate images; and comparing a key block of each of the plurality of candidate images with a key block of the template image to generate a difference map and a comparison result.

Abstract: According to an embodiment, a defect inspection method is performed with a defect inspection apparatus. The defect inspection apparatus is adapted to irradiate a first sample with illumination light to acquire a first sample image, and compare the first sample image to a reference image to inspect a defect. The defect inspection method includes generating the reference image, acquiring the first sample image, setting a defect detection condition using an index based on a result of defect inspection of a second sample different from the first sample, and discriminating a nuisance from a result of comparison between the reference image and the first sample image based on the defect detection condition.

Abstract: A display device includes a light emitting area and a sub-area disposed at a side of the light emitting area, a measurement area disposed in the sub-area, measurement patterns and a first electrode extension portion being disposed in the measurement area, a first electrode and a second electrode that are disposed in the light emitting area and spaced apart from each other, and face each other, a first insulating layer disposed on the first electrode and the second electrode, at least a part of the first insulating layer being disposed on the first electrode extension portion, and at least one light emitting element having ends disposed on the first electrode and the second electrode in the light emitting area. The measurement area includes a first measurement area in which a first measurement hole exposing a part of an upper surface of the first electrode extension portion is disposed.

Abstract: The disclosure discloses a method for detecting an intrusion in parallel based on an unbalanced data Deep Belief Network, which reads an unbalanced data set DS; under-samples the unbalanced data set using the improved NCR algorithm to reduce the ratio of the majority type samples and make the data distribution of the data set balanced; the improved differential evolution algorithm is used on the distributed memory computing platform Spark to optimize the parameters of the deep belief network model to obtain the optimal model parameters; extract the feature of data of the data set, and then classify the intrusion detection by the weighted nuclear extreme learning machine, and finally train multiple weighted nuclear extreme learning machines of different structures in parallel by multithreading as the base classifier, and establish a multi-classifier intrusion detection model based on adaptive weighted voting for detecting the intrusion in parallel.

Abstract: There is provided an inspection method that includes determining, as a reference blur component, the magnitude of the blur component of a range, the range being a range where a number of first inspection images among the plurality of inspection images falling within the range is the largest among a plurality of ranges. The inspection method includes correcting, based on the reference blur component, a second inspection image among the plurality of inspection images having the magnitude of the blur component falling outside the range. The inspection method includes comparing the first inspection image and the corrected second inspection image with a reference image, the first inspection image having the magnitude of the blur component falling within the predetermined range, the reference image being generated in advance for the measurement object.

Abstract: A method for constructing an efficient product surface defect detection model based on network collaborative pruning is provided. According to an initial product surface defect detection model, the method provides a network-based collaborative pruning method and constructs the efficient product surface defect detection model. On a premise of ensuring an accuracy of a product defect detection method, a product surface defect detection time is reduced to satisfy manufacturer's requirements on the product surface defect detection time and accuracy of product surface defects.

Abstract: A method, computer program product, and computer system for artboard element positioning in a series of computer-based artboards. The method includes providing multiple target artboards and identifying a common element in at least some of the target artboards, where a common element has at least some consistent attributes. The method includes determining a reference position for the common element. The method includes, in each target artboard containing the common element, comparing an existing position of the common element with the reference position and determining whether to adjust the existing position to match the reference position.

Abstract: A system, method and computer readable medium for examining a specimen, the method comprising: obtaining defects of interest (DOIs) and false alarms (FAs) from a review subset selected from a group of potential defects received from an inspection tool, each potential defect is associated with attribute values defining a location of the potential defect in an attribute space; generating a representative subset of the group, comprising potential defects selected in accordance with a distribution of the potential defects within the attribute space, and indicating the potential defects in the representative subset as FA; and training a classifier using data informative of the attribute values of the DOIs, the potential defects of the representative subset, and respective indications thereof as DOIs or FAs, wherein the trained classifier is to be applied to at least some of the potential defects to obtain an estimation of a number of expected DOIs.

Abstract: There is provided with an information processing device. A defect detecting unit detects a defect of an object in an input image. An extracting unit extracts a feature amount pertaining to a partial image of the defect from the input image, on the basis of a result of detecting the defect. An attribute determining unit determines an attribute of the defect using the feature amount pertaining to the partial image of the defect.

Abstract: A context-based inspection system is disclosed. The system may include an optical imaging sub-system. The system may further include one or more controllers communicatively coupled to the optical imaging system. The one or more controllers may be configured to: receive one or more reference images; receive one or more test images of a sample; generate one or more probabilistic context maps during inspection runtime using an unsupervised classifier; provide the generated one or more probabilistic context maps to a supervised classifier during the inspection runtime; and apply the supervised classifier to the received one or more test images to identify one or more DOIs on the sample.

Abstract: The present invention relates to a chip counter, which transmits an X-ray beam through a tape reel around which a tape having a plurality of semiconductor chips mounted in a row therein is wound, acquires an image scattered or diffracted by the semiconductor chips, and processes the acquired image, so as to count the number of the semiconductor chips, wherein: the X-ray beam transmitted through the tape reel (1) is sensed by a fluorescent intensifying screen (60); a fluorescent light emitted from the fluorescent intensifying screen (60) according to the sensing of the X-ray beam is captured by a camera (70), so that the number of the semiconductor chips is counted from an image in which the semiconductor chips are displayed by a dotted image; and the camera (70) is protected by an X-ray beam shielding member (100: 110; 120; and 130).

Abstract: Methods and systems for setting up care areas (CAs) for inspection of a specimen are provided. One system includes an imaging subsystem configured for generating images of a specimen and a computer subsystem configured for determining a number of defects detected in predefined cells within one or more of the images generated in a repeating patterned area formed on the specimen. The computer subsystem is also configured for comparing the number of the defects detected in each of two or more of the predefined cells to a predetermined threshold and designating any one or more of the two or more of the predefined cells in which the number of the defects is greater than the predetermined threshold as one or more CAs. In addition, the computer subsystem is configured for storing information for the one or more CAs for use in inspection of the specimen.

Abstract: A belt examination system includes a defect candidate detecting processor that detects a candidate for a belt defect of an intermediate transfer belt of an image forming apparatus from a belt image, the defect candidate detecting processor executes a background pattern reduction step to reduce a texture-pattern like background noise present in the belt image and detects the candidate based on the belt image generated during the background pattern reduction step, the background pattern reduction step is to execute a locally adaptive binarization process on the belt image in a range having a specific size based on a typical size of the belt defect to generate a binary image and subtract the binary image from the belt image, and the binary image includes a first value into which a lowest color value of the belt defect is converted and a second value larger than the first value.

Abstract: An image processing apparatus according to an embodiment includes a processor. The processor acquires a classification result of classifying each of a plurality of regions set in a processing target image into one of a plurality of predetermined classes. The processor converts multidimensional data corresponding to each of the plurality of regions set in the image into low-dimensional data. The processor causes a display image including one or more regions to be displayed together with a plotted diagram including a plurality of plot points having different colors or patterns applied according to the classification result to the low-dimensional data in each of the plurality of regions, and in a case where an instruction for selecting a selected point from among the plot points is issued, the processor performs a discriminative display causing the selected point and a region corresponding to the selected point in the display image to be visually identifiable.

Abstract: A computer-implemented method includes receiving a neural network model that includes a tensor operation, dividing the tensor operation into a set of sub-operations, and generating instructions for performing a plurality of sub-operations of the set of sub-operations on respective computing engines of a plurality of computing engines on a same integrated circuit device or on different integrated circuit devices. Each sub-operation of the set of sub-operations generates a portion of a final output of the tensor operation. An inference is made based on a result of a sub-operation of the plurality of sub-operations, or based on results of the plurality of sub-operations.

Abstract: A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Abstract: Disclosed are a polarizer attachment detection method and device, and a display device. The polarizer attachment detection method includes: controlling an image collection device to collect image data after attachment of a polarizer in a current detection mode; in response to the image data in the current detection mode failing to match prestored standard image data, switching to a next detection mode, controlling the image collection device to collect image data after the attachment of the polarizer in the next detection mode, and marking the image data collected in the next detection mode as new image data; in response to the new image data matching the prestored standard image data, outputting result information that the attachment is correct; and in response to the new image data failing to match the prestored standard image data, outputting result information that the attachment is incorrect.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: This image processing device, by superimposing a defect image 52 on a background image 51, with the original image of an inspection subject as the background image 51, and performing image processing in which the display format of the defect image 52 or the display format of the background image 51 is altered, a plurality of composite images 50 having different ways of seeing the defect image 52 with respect to the background image 51 are generated (step 403), the advisability of detecting the defect image 52 from each of the plurality of composite images 50 is verified (step 404), the detectable range 60 of the defect image 52 is estimated on the basis of the detection advisability verification results (step 405), and the detectable range 60 is displayed (step 406).

Abstract: One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.

Abstract: A workpiece inspection and defect detection system includes a light source configuration, a lens configuration, and a camera configuration for imaging workpieces. The system acquires training and run mode workpiece images for acquiring corresponding sets of training and run mode workpiece image data. Each set of image data includes at least first and second color channel workpiece image data corresponding to first and second color channels (e.g., for which ratios between the first and second color channel workpiece image data may be determined as part of synthetic image data to improve the ability of the system to detect defects). The defect detection portion is trained based at least in part on the image data, and is utilized to perform analysis to determine defect images that include workpieces with defects (e.g., for which metrology operations may be performed for measuring dimensions of defects, etc.).

Abstract: Disclosed herein are methods and systems of metadata management for reviewing data from microscopy experimental sessions. Image data from an experimental session is stored in an archive at one or more filepath locations, either locally or on a network. Metadata associated with the image data is stored in a database with a reference to the filepath where the raw image is stored, such that the metadata is associated in the database with the image data. A user can perform post-experimental filtering, sorting, and searching of the underlying image data using the metadata, which allows the image data to be analyzed without duplication of the image data and without manual review of each individual image. The filtered data is presented in an interactive timeline format.

Abstract: In a case where an inspection level to be used in determining quality of an image of an inspected surface of a printed sheet is set based on a comparison between a captured image resulting from imaging the inspected surface and a reference image, a plurality of inspection regions varying in inspection level is set for the reference image. In a case where an overlap region is present in the plurality of inspection regions, an inspection level of the overlap region is identified based on a predetermined priority. Further, the identified inspection level of the overlap region is displayed.

Abstract: Provided is a method of inspecting a growth quality of a graphene layer of a graphene-grown copper foil obtained by growing the graphene layer on a copper foil layer by chemical vapor deposition (CVD), the method including reacting oxygen or water molecules with the copper foil layer via a defect portion of the graphene layer, partitioning an entire region of the graphene-grown copper foil into partial regions, sequentially obtaining images of the partial regions, detecting, with respect to each of the images of the partial regions, an oxidized region where the copper foil layer is oxidized, and setting the oxidized region as a graphene defect region, and obtaining a ratio of an area of the graphene defect region to an entire area of each of the images of the partial regions.

Abstract: An object of the present disclosure is to provide a system for deriving a type of a defect of a semiconductor element and a non-transitory computer-readable medium. The system receives, from the image acquisition tool, image data obtained by sequentially irradiating a plurality of patterns provided on the semiconductor wafer with a beam and extracts characteristics of the plurality of patterns sequentially irradiated with a beam from the received image data, the characteristics being included in the image data, or receives characteristics of the plurality of patterns sequentially irradiated with a beam from the image acquisition tool, the characteristics being extracted from the image data (Step 603), and derives (Step 605) a type of a defect by referring to (Step 604) related information for the characteristics of the plurality of patterns, the related information storing the characteristics of the plurality of patterns and types of defects in association with each other.

Abstract: Common events between layers on a semiconductor wafer are filtered. Common events should contain the majority of defects of interest. Only nuisance events that are common between layers on the semiconductor wafer remain, which reduces the nuisance rate. Defects that are common across layers can be filtered based on, for example, defect coordinates, a difference image, or defect attributes.

Abstract: Disclosed is a computerized method for detecting defects on a sample. The method includes: (i) receiving scan data corresponding to a pixel on the sample; (ii) computing a difference vector d based on the scan data and corresponding reference data; (iii) computing a parameter D dependent on t=?d?(Glinear/??s?2)?s, wherein ?T?=K?1 with K being a covariance matrix corresponding to the pixel, s is a predetermined kernel characterizing a defect signal, and Glinear=s·(K?1 d) is a gaussian approximation of a likelihood ratio test expression for distinguishing the defect signal from noise, and wherein D substantially monotonically increases with ?t?; and (iv) computing a score q(g, D) indicative of whether the pixel is defective, wherein g is a parameter indicative of a value of Glinear and q(g, D) substantially monotonically increases with g and substantially monotonically decreases with D.

Abstract: A system for use in repairing a stamping press die. The system includes an imaging system configured to perform a scan on a pressing surface of the stamping press die, wherein a profile of the pressing surface is determined based on the scan. A computing device is configured to compare the determined profile to a desired profile of the pressing surface, and to generate a repair template based on dimensional variations between the determined profile and the desired profile. A projection system is configured to display the repair template on the pressing surface.

Abstract: A method for manufacturing a semiconductor device includes designing a layout, manufacturing a photomask based on the designed layout, and performing a photolithography process using the photomask to form a pattern on a substrate. The manufacturing of the photomask includes preparing the photomask including first and second chip regions, extracting first and second images from the first and second chip regions, respectively, averaging the first and second images to generate a preliminary standard image including a difference region between the first and second images, inserting a normal image into the difference region based on the layout to generate a standard image, and comparing each of the first and second images with the standard image to detect a defect of the first and/or second chip regions.

Abstract: There is provided a system and method of a method of detecting a local shape deviation of a structural element in a semiconductor specimen, comprising: obtaining an image comprising an image representation of the structural element; extracting, from the image, an actual contour of the image representation; estimating a reference contour of the image representation indicative of a standard shape of the structural element, wherein the reference contour is estimated based on a Fourier descriptor representative of the reference contour, the Fourier descriptor being estimated using an optimization method based on a loss function specifically selected to be insensitive to local shape deviation of the actual contour; and performing one or more measurements representative of one or more differences between the actual contour and the reference contour, the measurements indicative of whether a local shape deviation is present in the structural element.

Abstract: Micro light emitting diode inspection and repairing equipment including a carrying stage, an optical inspection module and an injection device is provided. The optical inspection module is arranged corresponding to the carrying stage to capture image information and obtain a position coordinate from the image information. The injection device is adapted to move to a target position of the carrying stage according to the position coordinate. The injection device includes a tube and a nozzle. The tube includes a first portion and a second portion connected to the first portion. The extending direction of the first portion is different from the extending direction of the second portion. A fluid blows to the target position after passing through the tube and the nozzle. An inspection and repairing method adopting the micro light emitting diode inspection and repairing equipment is also provided.