Defect detection of a semiconductor specimen

    公开(公告)号:US12223641B2

    公开(公告)日:2025-02-11

    申请号:US17748996

    申请日:2022-05-19

    Abstract: There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

    Automatic optimization of an examination recipe

    公开(公告)号:US12007335B2

    公开(公告)日:2024-06-11

    申请号:US18196655

    申请日:2023-05-12

    Inventor: Amir Bar

    CPC classification number: G01N21/9501 G06T7/0004 G06T2207/30148

    Abstract: A method of automatic optimization of an examination recipe includes obtaining inspection data of a given layer of a semiconductor specimen acquired by an inspection tool during runtime examination, the inspection data including inspection images representative of defect candidates from a defect map of the given layer, extracting inspection features characterizing the inspection images, and using a classifier to classify the defect candidates based on the inspection features, giving rise to a list of defect candidates having a higher probability of being defects of interest (DOIs). The semiconductor specimen includes multiple layers, and the classifier is a general-purpose classifier (GPC) usable for runtime classification of inspection data from any layer of the multiple layers of the semiconductor specimen, the GPC being previously trained using training data including inspection features characterizing training inspection images of various types of DOIs and nuisances collected from the multiple layers and label data associated therewith.

    Automatic optimization of an examination recipe

    公开(公告)号:US11686689B2

    公开(公告)日:2023-06-27

    申请号:US17697063

    申请日:2022-03-17

    Inventor: Amir Bar

    CPC classification number: G01N21/9501 G06T7/0004 G06T2207/30148

    Abstract: There is provided a system and method of automatic optimization of an examination recipe. The method includes obtaining one or more inspection images each representative of at least a portion of the semiconductor specimen, the one or more inspection images being indicative of respective defect candidates selected from a defect map using a first classifier included in the examination recipe; obtaining label data respectively associated with the one or more inspection images and informative of types of the respective defect candidates; extracting inspection features characterizing the one or more inspection images; retraining the first classifier using the first features and the label data, giving rise to a second classifier; and optimizing the examination recipe by replacing the first classifier with the second classifier; wherein the optimized examination recipe is usable for examining a subsequent semiconductor specimen.

    Automatic optimization of an examination recipe

    公开(公告)号:US11307150B2

    公开(公告)日:2022-04-19

    申请号:US16995728

    申请日:2020-08-17

    Inventor: Amir Bar

    Abstract: There is provided a system and method of automatic optimization of an examination recipe. The method includes obtaining one or more inspection images each representative of at least a portion of the semiconductor specimen, the one or more inspection images being indicative of respective defect candidates selected from a defect map using a first classifier included in the examination recipe; obtaining label data respectively associated with the one or more inspection images and informative of types of the respective defect candidates; extracting inspection features characterizing the one or more inspection images; retraining the first classifier using the first features and the label data, giving rise to a second classifier; and optimizing the examination recipe by replacing the first classifier with the second classifier; wherein the optimized examination recipe is usable for examining a subsequent semiconductor specimen.

    METHOD OF EXAMINING SPECIMENS AND SYSTEM THEREOF

    公开(公告)号:US20220291138A1

    公开(公告)日:2022-09-15

    申请号:US17829593

    申请日:2022-06-01

    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.

    Selecting a coreset of potential defects for estimating expected defects of interest

    公开(公告)号:US11360030B2

    公开(公告)日:2022-06-14

    申请号:US16782005

    申请日:2020-02-04

    Abstract: Disclosed is a system, method and computer readable medium for selecting a coreset of potential defects for estimating expected defects of interest. An example method includes obtaining a plurality of defects of interest (DOIs) and false alarms (FAs) from a review subset selected from a group of potential defects received from an inspection tool. The method further includes generating a representative subset of the group of potential defects. The representative subset includes potential defects selected in accordance with a distribution of the group of potential defects within an attribute space. The method further includes, 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.

Patent Agency Ranking