DETECTING DEFECTS IN SEMICONDUCTOR SPECIMENS USING WEAK LABELING

    公开(公告)号:US20210383530A1

    公开(公告)日:2021-12-09

    申请号:US16892139

    申请日:2020-06-03

    Abstract: A system of classifying a pattern of interest (POI) on a semiconductor specimen, the system comprising a processor and memory circuitry configured to: obtain a high-resolution image of the POI, and generate data usable for classifying the POI in accordance with a defectiveness-related classification, wherein the generating utilizes a machine learning model that has been trained in accordance with training samples comprising: a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI, and a label associated with the image, the label being derivative of low-resolution inspection of the respective training pattern.

    Method Of Generating A Training Set Usable For Examination Of A Semiconductor Specimen And System Thereof

    公开(公告)号:US20200226420A1

    公开(公告)日:2020-07-16

    申请号:US16631155

    申请日:2019-02-07

    Abstract: There is provided a method of examination of a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising synthetic images specific for the given application; and obtaining, by the computer, examination-related data specific for the given application, and characterizing at least one of the processed one or more FP images. Generating the training set can comprise: training an auxiliary DNN to generate a latent space, generating a synthetic image by applying the trained auxiliary DNN to a point selected in the generated latent space, and adding the generated synthetic image to the training set.

    SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR CLASSIFYING DEFECTS

    公开(公告)号:US20190293669A1

    公开(公告)日:2019-09-26

    申请号:US15933306

    申请日:2018-03-22

    Abstract: An examination system, a method of obtaining a training set for a classifier, and a non-transitory computer readable medium, the method comprising: upon receiving in a memory device object inspection results comprising data indicative of potential defects, each potential defect of the potential defects associated with a multiplicity of attribute values defining a location of the potential defect in an attribute space: sampling by the processor a first set of defects from the potential defects, wherein the defects within the first set are dispersed independently of a density of the potential defects in the attribute space; and obtaining by the processor a training defect sample set comprising the first set of defects and data or parameters representative of the density of the potential defects in the attribute space.

    MASK INSPECTION OF A SEMICONDUCTOR SPECIMEN

    公开(公告)号:US20210073963A1

    公开(公告)日:2021-03-11

    申请号:US16833380

    申请日:2020-03-27

    Abstract: There is provided a mask inspection system and a method of mask inspection. The method comprises: during a runtime scan of a mask of a semiconductor specimen, processing a plurality of aerial images of the mask acquired by the mask inspection system to calculate a statistic-based Edge Positioning Displacement (EPD) of a potential defect, wherein the statistic-based EPD is calculated using a Print Threshold (PT) characterizing the mask and is applied to each of the one or more acquired aerial images to calculate respective EPD of the potential defect therein; and filtering the potential defect as a “runtime true” defect when the calculated statistic-based EPD exceeds a predefined EPD threshold, and filtering out the potential defect as a “false” defect when the calculated statistic-based EPD is lower than the predefined EPD threshold. The method can further comprise after-runtime EPD-based filtering of the plurality of “runtime true” defects.

    GENERATING A TRAINING SET USABLE FOR EXAMINATION OF A SEMICONDUCTOR SPECIMEN

    公开(公告)号:US20190257767A1

    公开(公告)日:2019-08-22

    申请号:US16280869

    申请日:2019-02-20

    Abstract: There is provided a system and method of generating a training set usable for examination of a semiconductor specimen. The method comprises: obtaining a simulation model capable of simulating effect of a physical process on fabrication process (FP) images depending on the values of parameters of the physical process; applying the simulation model to an image to be augmented for the training set and thereby generating one or more augmented images corresponding to one or more different values of the parameters of the physical process; and including the generated one or more augmented images into the training set. The training set can be usable for examination of the specimen using a trained Deep Neural Network, automated defect review, automated defect classification, automated navigation during the examination, automated segmentation of FP images, automated metrology based on FP images and other examination processes that include machine learning.

    AUTOMATIC SELECTION OF ALGORITHMIC MODULES FOR EXAMINATION OF A SPECIMEN

    公开(公告)号:US20210343000A1

    公开(公告)日:2021-11-04

    申请号:US16866463

    申请日:2020-05-04

    Abstract: There is provided a system comprising a processor configured to obtain a set of images of a semiconductor specimen, (1) for an image of the set of images, select at least one algorithmic module MS out of a plurality of algorithmic modules, (2) feed the image to MS to obtain data DMS representative of one or more defects in the image, (3) obtain a supervised feedback regarding rightness of data DMS, (4) repeat (1) to (3) for a next image until a completion criterion is met, wherein an algorithmic module selected at (1) is different for at least two different images of the set of images, generate, based on the supervised feedback, a score for each of a plurality of the algorithmic modules, and use scores to identify one or more algorithmic modules Mbest as the most adapted for providing data representative of one or more defects in the set of images.

    METHOD OF EXAMINING SPECIMENS AND SYSTEM THEREOF

    公开(公告)号:US20210239623A1

    公开(公告)日:2021-08-05

    申请号:US16782005

    申请日:2020-02-04

    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 upon 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, applying the classifier to at least some of the potential defects to obtain an estimation of a number of expected DOIs in the specimen.

    SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR CLASSIFYING A MULTIPLICITY OF ITEMS

    公开(公告)号:US20190095800A1

    公开(公告)日:2019-03-28

    申请号:US15719433

    申请日:2017-09-28

    Abstract: A system, method and computer software product, the system capable of classifying defects and comprising: an hardware-based GUI component; and a processing and memory circuitry configured to: a. upon obtaining data informative of a plurality of defects and attribute values thereof, using the attribute values to create initial classification of the plurality of defects into a plurality of classes; b. for a given class, presenting to a user, by the hardware-based GUI component, an image of a defect initially classified to the given class with a low likelihood, wherein the image is presented along with images of one or more defects initially classified to the given class with the highest likelihood; and c. subject to confirming by the user, using the hardware-based GUI component, that the at least one defect is to be classified to the given class, indicating the at least one defect as belonging to the given class.

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