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    System and method for difference filter and aperture selection using shallow deep learning
    2.
    发明授权
    System and method for difference filter and aperture selection using shallow deep learning 有权

    公开(公告)号：US11151707B2

    公开(公告)日：2021-10-19

    申请号：US16277769

    申请日：2019-02-15

    Applicant: KLA-Tencor Corporation

    Inventor： Santosh Bhattacharyya ,  Jacob George ,  Saravanan Paramasivam ,  Martin Plihal

    IPC: G06T7/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G01N23/2251

    Abstract: A system for defect review and classification is disclosed. The system may include a controller, wherein the controller may be configured to receive one or more training images of a specimen. The one or more training images including a plurality of training defects. The controller may be further configured to apply a plurality of difference filters to the one or more training images, and receive a signal indicative of a classification of a difference filter effectiveness metric for at least a portion of the plurality of difference filters. The controller may be further configured to generate a deep learning network classifier based on the received classification and the attributes of the plurality of training defects. The controller may be further configured to extract convolution layer filters of the deep learning network classifier, and generate one or more difference filter recipes based on the extracted convolution layer filters.

    Alignment of inspection to design using built in targets
    6.
    发明授权
    Alignment of inspection to design using built in targets 有权

    公开(公告)号：US09830421B2

    公开(公告)日：2017-11-28

    申请号：US14983452

    申请日：2015-12-29

    Applicant: KLA-Tencor Corporation

    Inventor： Santosh Bhattacharyya ,  Bjorn Braeuer ,  Lisheng Gao

    IPC: G06F17/50 ,  G03F7/20

    CPC classification number: G06F17/5081 ,  G03F7/70616

    Abstract: Methods and systems for determining a position of output generated by an inspection subsystem in design data space are provided. One method includes selecting one or more alignment targets from a design for a specimen. At least a portion of the one or more alignment targets include built in targets included in the design for a purpose other than alignment of inspection results to design data space. At least the portion of the one or more alignment targets does not include one or more individual device features. One or more images for the alignment target(s) and output generated by the inspection subsystem at the position(s) of the alignment target(s) may then be used to determine design data space positions of other output generated by the inspection subsystem in a variety of ways described herein.

    System and Method for Difference Filter and Aperture Selection Using Shallow Deep Learning
    7.
    发明申请
    System and Method for Difference Filter and Aperture Selection Using Shallow Deep Learning 审中-公开

    公开(公告)号：US20200184628A1

    公开(公告)日：2020-06-11

    申请号：US16277769

    申请日：2019-02-15

    Applicant: KLA-Tencor Corporation

    Inventor： Santosh Bhattacharyya ,  Jacob George ,  Saravanan Paramasivam ,  Martin Plihal

    IPC: G06T7/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G01N23/2251

    Abstract: A system for defect review and classification is disclosed. The system may include a controller, wherein the controller may be configured to receive one or more training images of a specimen. The one or more training images including a plurality of training defects. The controller may be further configured to apply a plurality of difference filters to the one or more training images, and receive a signal indicative of a classification of a difference filter effectiveness metric for at least a portion of the plurality of difference filters. The controller may be further configured to generate a deep learning network classifier based on the received classification and the attributes of the plurality of training defects. The controller may be further configured to extract convolution layer filters of the deep learning network classifier, and generate one or more difference filter recipes based on the extracted convolution layer filters.

    Combined patch and design-based defect detection
    8.
    发明授权
    Combined patch and design-based defect detection 有权

    公开(公告)号：US10192302B2

    公开(公告)日：2019-01-29

    申请号：US15356799

    申请日：2016-11-21

    Applicant: KLA-Tencor Corporation

    Inventor： Bjorn Brauer ,  Santosh Bhattacharyya

    IPC: G06K9/62 ,  G06T7/00

    Abstract: Defect detection is performed by comparing a test image and a reference image with a rendered design image, which may be generated from a design file. This may occur because a comparison of the test image and another reference image was inconclusive due to noise. The results of the two comparisons with the rendered design image can indicate whether a defect is present in the test image.

    Inspection for specimens with extensive die to die process variation
    9.
    发明授权
    Inspection for specimens with extensive die to die process variation 有权

    公开(公告)号：US10151706B1

    公开(公告)日：2018-12-11

    申请号：US15481421

    申请日：2017-04-06

    Applicant: KLA-Tencor Corporation

    Inventor： Santosh Bhattacharyya ,  Hucheng Lee ,  Bjorn Brauer

    IPC: G01N21/95 ,  G01N21/88

    Abstract: Methods and systems for detecting defects on a specimen are provided. One method includes identifying first and second portions of dies on a specimen as edge dies and center dies, respectively. The method also includes determining first and second inspection methods for the first and second portions, respectively. Parameter(s) of comparisons performed in the first and second inspection methods are different. The method further includes detecting defects in at least one of the edge dies using the first inspection method and detecting defects in at least one of the center dies using the second inspection method.

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