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    INSPECTION OF RETICLES USING MACHINE LEARNING
    1.
    发明申请
    INSPECTION OF RETICLES USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20190206041A1

    公开(公告)日：2019-07-04

    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

    CPC classification number: G06T7/001 ,  G06T2207/20081 ,  G06T2207/20084

    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.

    Inspection of reticles using machine learning
    2.
    发明授权
    Inspection of reticles using machine learning 有权

    公开(公告)号：US12094101B2

    公开(公告)日：2024-09-17

    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

    CPC classification number: G06T7/001 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

    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.

    CRITICAL DIMENSION UNIFORMITY ENHANCEMENT TECHNIQUES AND APPARATUS
    4.
    发明申请
    CRITICAL DIMENSION UNIFORMITY ENHANCEMENT TECHNIQUES AND APPARATUS 审中-公开
    关键尺寸均匀性增强技术和设备

    公开(公告)号：US20160110858A1

    公开(公告)日：2016-04-21

    申请号：US14884670

    申请日：2015-10-15

    Applicant: KLA-Tencor Corporation

    Inventor： Yanwei Liu ,  Hawren Fang

    IPC: G06T7/00 ,  G06T7/60 ,  G06K9/52 ,  G06K9/62

    CPC classification number: G06K9/6218 ,  G03F1/84 ,  G06K9/036 ,  G06T7/001 ,  G06T7/41 ,  G06T7/60 ,  G06T2207/20056 ,  G06T2207/30148

    Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. Modeled images of a plurality of target features of the reticle are obtained based on a design database for fabricating the reticle. An inspection tool is used to obtain a plurality of actual images of the target features of the reticle. The modelled and actual images are binned into a plurality of bins based on image properties of the modelled and actual images, and at least some of the image properties are affected by one or more neighbor features of the target features on the reticle in a same manner. The modelled and actual images from at least one of the bins are analyzed to generate a feature characteristic uniformity map for the reticle.

    Abstract translation: 公开了用于检查光刻掩模版的方法和装置。 基于用于制造掩模版的设计数据库获得掩模版的多个目标特征的建模图像。 使用检查工具来获得掩模版的目标特征的多个实际图像。 基于建模和实际图像的图像属性，将建模的和实际的图像分为多个箱，并且至少一些图像属性以相同的方式受到掩模版上的目标特征的一个或多个相邻特征的影响 。 分析来自至少一个箱体的建模和实际图像，以生成分划板的特征特征均匀性图。

    INSPECTION OF RETICLES USING MACHINE LEARNING
    5.
    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
    6.
    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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