Disclosed herein are systems and methods for synthesizing a diamond using a diamond synthesis machine. A processor receives a plurality of images of a diamond during synthesis within a diamond synthesis machine, each of the plurality of images captured within a time period. The processor executes a diamond state prediction machine learning model using the plurality of images to obtain a predicted data object, the predicted data object indicating a predicted state of the diamond within the diamond synthesis machine at a time subsequent to the time period. The processor detects a predicted defect, a number of defects, defect types, and/or sub-features of such defects and/or other characteristics (e.g., a predicted shape, size, and/or other properties of predicted contours for the diamond and/or pocket holder) of the predicted state of the diamond. The processor adjusts operation of the diamond synthesis machine.

The invention is to provide a defect detection device capable of using a compact optical system to detect a plurality of types of defects with high sensitivity and high speed. The defect detection device includes an irradiation system that irradiates light onto an object to be inspected; an optical system that forms scattered light produced by a light irradiation into an image; a microlens array disposed at an image plane of the optical system; an imaging element that is disposed at a position offset from the imaging plane of the optical system and that images light that passes through the microlens array; a mask image storage unit that stores a plurality of mask images generated for each type of defect or each defect direction; and a calculation unit that carries out mask processing on an image obtained from the imaging element using the plurality of mask images and carries out defect detection processing.

There is provided a system and a method comprising obtaining a sequence of a plurality of frames of an area of a specimen, wherein at least one frame of the sequence is transformed with respect to another frame, obtaining a reference frame based at least on a first frame of the sequence, determining, based on the reference frame, a reference pattern, wherein the reference pattern is informative of a structural feature of the specimen in the area, for a given frame of the sequence, determining, based on the given frame, a pattern informative of said structural feature in the area, determining data D.sub.shrinkage informative of an amplitude of a spatial transformation between the reference pattern and the pattern, generating a corrected frame based on said pattern and D.sub.shrinkage and generating an image of the area.

A method for automatic defect classification, the method may include acquiring, by a first camera, at least one first image of at least one area of an object; processing the at least one first image to detect a group of suspected defects within the at least one area; performing a first classification process for initially classifying the group of suspected defects; determining whether a completion of a classification of the first subgroup of the suspected defects requires additional information from a second camera; when determining that the first subgroup of the suspected defects requires additional information from the second camera then: acquiring second images, by the second camera while applying image acquisition parameters of the second camera, to provide the additional information; and performing the second classification process for classifying the first subgroup of suspected defects.

A method for detecting and classifying defects in high-throughput transparent articles such as syringes, vials, cartridges, ampules, and bottles is provided. The method includes the steps of providing a stream of the articles; capturing a first digital image of each of the articles in the stream; inspecting the first digital image for objects; determining parameters of the objects; performing a first classification step to classify the objects into a first defect class and a second defect class; performing a second classification step to classify the objects into a plurality of defect types using at least two second classification models; comparing at least one object parameter of a classified object with a predetermined defect type dependent threshold; classifying the article as defective or non-defective based on the comparing step; and separating defective articles from non-defective articles.

A method and system for performing inspection of a manufactured article includes acquiring a sequence of images using an image acquisition device of the article under inspection. The sequence of images is acquired while relative movement between the article and the image acquisition device is caused. At least one feature characterizing the manufactured article is extracted from the acquired sequence of images. The acquired sequence of images is classified based in part on the extracted feature. The classification may include determining an indication, of a presence of a manufacturing defect in the article, and may include identifying a type of manufacturing defect. The extracting and the classifying can be performed by a computer-implemented classification module, which may be trained by machine learning techniques.

There is provided a mask inspection system and a method of mask inspection. The method comprises: detecting, by the inspection tool, a runtime defect at a defect location on a mask of a semiconductor specimen during runtime scan of the mask, and acquiring, by the inspection tool after runtime and based on the defect location, a plurality sets of aerial images of the runtime defect corresponding to a plurality of focus states throughout a focus process window, each set of aerial images acquired at a respective focus state. The method further comprises for each set of aerial images, calculating a statistic-based EPD value of the runtime defect, thereby giving rise to a plurality of statistic-based EPD values each corresponding to a respective focus state, and determining whether the runtime defect is a true defect based on the plurality of statistic-based EPD values.

A detection device, a detection apparatus, and a detection method are provided. The detection device includes a detection assembly and a driving assembly. At least two rows of detection units are provided, and the detection units are arranged in a matrix along the first direction and the second direction. During detection, the driving assembly may drive one or both of the to-be-detected object and the detection assembly to move along at least one of the first direction and the second direction, so that the detection units scan different areas of the surface of the to-be-detected object, thereby realizing a scanning of the entire surface. That is, the scanning of the surface of the to-be-detected object is completed by using multiple detection units, which improves detection efficiency for the to-be-detected surface, and realizes a relatively high imaging quality.

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.

A method for automatic defect classification, the method may include (i) acquiring, by a first camera, at least one first image of at least one area of an object; (ii) processing the at least one first image to detect a group of suspected defects within the at least one area; (iii) performing a first classification process for initially classifying the group of suspected defects; (iii) determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification; (iv) when determining that the first subgroup of the suspected defects requires additional information from the second camera then: (a) acquiring second images, by the second camera, of the first subgroup of the suspected defects; and (b) performing a second classification process for classifying the first subgroup of suspected defects.