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    Apparatus and methods for inspecting reticles
    11.
    发明授权
    Apparatus and methods for inspecting reticles 有权

    公开(公告)号：US10395361B2

    公开(公告)日：2019-08-27

    申请号：US15803628

    申请日：2017-11-03

    Applicant: KLA-Tencor Corporation

    Inventor： Abdurrahman Sezginer ,  Mohammad Mehdi Daneshpanah

    IPC: G06T7/00 ,  G01N21/956 ,  G03F1/84 ,  G03F7/20

    Abstract: Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire a plurality of images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field is recovered for each of the pattern areas of the test reticle based on the acquired images from each pattern area of the test reticle. The recovered reticle near field is then used to determine whether the test reticle or another reticle will likely result in unstable wafer pattern or a defective wafer.

    Model-Based Registration and Critical Dimension Metrology
    14.
    发明申请
    Model-Based Registration and Critical Dimension Metrology 有权
    基于模型的注册和关键维度计量学

    公开(公告)号：US20140086475A1

    公开(公告)日：2014-03-27

    申请号：US14032309

    申请日：2013-09-20

    Applicant: KLA-Tencor Corporation

    Inventor： Mohammad Mehdi Daneshpanah ,  Abdurrahman Sezginer

    IPC: G06T7/00

    CPC classification number: G06T7/001 ,  G06T2207/10056 ,  G06T2207/30148

    Abstract: A method and system for performing model-based registration and critical dimension measurement is disclosed. The method includes: utilizing an imaging device to obtain at least one optical image of a measurement site specified for a photomask; retrieving a design of photomask and utilizing a computer model of the imaging device to generate at least one simulated image of the measurement site; adjusting at least one parameter of the computer model to minimize dissimilarities between the simulated images and the optical images, wherein the parameters includes at least a pattern registration parameter or a critical dimension parameter; and reporting the pattern registration parameter or the critical dimension parameter of the computer model when dissimilarities between the simulated images and the optical images are minimized.

    Abstract translation: 公开了一种用于执行基于模型的注册和关键维度测量的方法和系统。 该方法包括：利用成像装置获得为光掩模指定的测量部位的至少一个光学图像; 检索光掩模的设计并利用所述成像装置的计算机模型来生成所述测量部位的至少一个模拟图像; 调整所述计算机模型的至少一个参数以最小化所述模拟图像与所述光学图像之间的不相似性，其中所述参数至少包括模式注册参数或临界尺寸参数; 并且当模拟图像和光学图像之间的不相似度最小化时报告计算机模型的模式注册参数或临界尺寸参数。

    INSPECTION OF RETICLES USING MACHINE LEARNING
    16.
    发明申请
    INSPECTION OF RETICLES USING MACHINE LEARNING 有权

    公开(公告)号：US20220084179A1

    公开(公告)日：2022-03-17

    申请号：US17456415

    申请日：2021-11-24

    Applicant: KLA-Tencor Corporation

    Inventor： Hawren Fang ,  Abdurrahman Sezginer ,  Rui-fang Shi

    IPC: G06T7/00

    Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. A plurality of reference far field images are simulated by inputting a plurality of reference near field images into a physics-based model, and the plurality of reference near field images are generated by a trained deep learning model from a test portion of the design database that was used to fabricate a test area of a test reticle. The test area of a test reticle, which was fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing the plurality of reference far field reticle images simulated by the physic-based model to a plurality of test images acquired by the inspection system from the test area of the test reticle.

    Inspection of reticles using machine learning
    17.
    发明授权
    Inspection of reticles using machine learning 有权

    公开(公告)号：US11257207B2

    公开(公告)日：2022-02-22

    申请号：US16201788

    申请日：2018-11-27

    Applicant: KLA-Tencor Corporation

    Inventor： Hawren Fang ,  Abdurrahman Sezginer ,  Rui-fang Shi

    IPC: G06T7/00

    Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. A near field reticle image is generated via a deep learning process based on a reticle database image produced from a design database, and a far field reticle image is simulated at an image plane of an inspection system via a physics-based process based on the near field reticle image. The deep learning process includes training a deep learning model based on minimizing differences between the far field reticle images and a plurality of corresponding training reticle images acquired by imaging a training reticle fabricated from the design database, and such training reticle images are selected for pattern variety and are defect-free. A test area of a test reticle, which is fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing a plurality of references images from a reference far field reticle image to a plurality of test images acquired by the inspection system from the test reticle. The reference far field reticle image is simulated based on a reference near field reticle image that is generated by the trained deep learning model.

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