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    Adaptive Nuisance Filter
    12.
    发明申请
    Adaptive Nuisance Filter 有权
    自适应扰动滤波器

    公开(公告)号：US20160258879A1

    公开(公告)日：2016-09-08

    申请号：US15058115

    申请日：2016-03-01

    Applicant: KLA-Tencor Corporation

    Inventor： Ardis Liang ,  Martin Plihal ,  Raghav Babulnath ,  Sankar Venkataraman

    IPC: G01N21/88 ,  G01N23/20 ,  G01N21/95

    CPC classification number: G01N21/8806 ,  G01N21/9501 ,  G01N23/20008 ,  G01N2021/8809 ,  G01N2201/061 ,  G01N2201/0683

    Abstract: Methods and systems for generating inspection results for a specimen with an adaptive nuisance filter are provided. One method includes selecting a portion of events detected during inspection of a specimen having values for at least one feature of the events that are closer to at least one value of at least one parameter of the nuisance filter than the values for at least one feature of another portion of the events. The method also includes acquiring output of an output acquisition subsystem for the sample of events, classifying the events in the sample based on the acquired output, and determining if one or more parameters of the nuisance filter should be modified based on results of the classifying. The nuisance filter or the modified nuisance filter can then be applied to results of the inspection of the specimen to generate final inspection results for the specimen.

    Abstract translation: 提供了一种用于产生具有自适应扰动滤波器的样本检测结果的方法和系统。 一种方法包括选择在样本检查期间检测到的事件的一部分，该样本具有关于事件的至少一个特征的值，该值至少与有害过滤器的至少一个参数的值相比， 另一部分事件。 该方法还包括获取用于事件采样的输出采集子系统的输出，基于所获取的输出对样本中的事件进行分类，以及基于分类结果确定是否应该修改妨扰滤波器的一个或多个参数。 然后可以将妨扰过滤器或修改后的滋扰过滤器应用于样品的检查结果，以产生样品的最终检验结果。

    Training a machine learning model with synthetic images
    14.
    发明授权
    Training a machine learning model with synthetic images 有权

    公开(公告)号：US11170255B2

    公开(公告)日：2021-11-09

    申请号：US16357360

    申请日：2019-03-19

    Applicant: KLA-Tencor Corporation

    Inventor： Ian Riley ,  Li He ,  Sankar Venkataraman ,  Michael Kowalski ,  Arjun Hegde

    IPC: G06K9/00 ,  G06K9/62 ,  G06N20/00 ,  G06F3/0482 ,  G06T7/00 ,  G06N3/08

    Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.

    Unified neural network for defect detection and classification
    15.
    发明授权
    Unified neural network for defect detection and classification 有权

    公开(公告)号：US10607119B2

    公开(公告)日：2020-03-31

    申请号：US15697426

    申请日：2017-09-06

    Applicant: KLA-Tencor Corporation

    Inventor： Li He ,  Mohan Mahadevan ,  Sankar Venkataraman ,  Huajun Ying ,  Hedong Yang

    IPC: G06K9/62 ,  G06T7/00 ,  G06K9/46

    Abstract: Methods and systems for detecting and classifying defects on a specimen are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for detecting defects on a specimen and classifying the defects detected on the specimen. The neural network includes a first portion configured for determining features of images of the specimen generated by an imaging subsystem. The neural network also includes a second portion configured for detecting defects on the specimen based on the determined features of the images and classifying the defects detected on the specimen based on the determined features of the images.

    Contour based defect detection
    16.
    发明授权
    Contour based defect detection 有权

    公开(公告)号：US10395362B2

    公开(公告)日：2019-08-27

    申请号：US15896060

    申请日：2018-02-14

    Applicant: KLA-Tencor Corporation

    Inventor： Ajay Gupta ,  Mohan Mahadevan ,  Sankar Venkataraman ,  Hedong Yang ,  Laurent Karsenti ,  Yair Carmon ,  Noga Bullkich ,  Udy Danino

    IPC: G06T7/00 ,  G06T7/564 ,  G01N21/95 ,  G01N21/88 ,  G01N21/956

    Abstract: Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

    Adaptive nuisance filter
    17.
    发明授权
    Adaptive nuisance filter 有权

    公开(公告)号：US09835566B2

    公开(公告)日：2017-12-05

    申请号：US15058115

    申请日：2016-03-01

    Applicant: KLA-Tencor Corporation

    Inventor： Ardis Liang ,  Martin Plihal ,  Raghav Babulnath ,  Sankar Venkataraman

    IPC: G01N21/88 ,  G01N23/20 ,  G01N21/95

    CPC classification number: G01N21/8806 ,  G01N21/9501 ,  G01N23/20008 ,  G01N2021/8809 ,  G01N2201/061 ,  G01N2201/0683

    Abstract: Methods and systems for generating inspection results for a specimen with an adaptive nuisance filter are provided. One method includes selecting a portion of events detected during inspection of a specimen having values for at least one feature of the events that are closer to at least one value of at least one parameter of the nuisance filter than the values for at least one feature of another portion of the events. The method also includes acquiring output of an output acquisition subsystem for the sample of events, classifying the events in the sample based on the acquired output, and determining if one or more parameters of the nuisance filter should be modified based on results of the classifying. The nuisance filter or the modified nuisance filter can then be applied to results of the inspection of the specimen to generate final inspection results for the specimen.

    Semiconductor inspection and metrology systems for distributing job among the CPUs or GPUs based on logical image processing boundaries
    18.
    发明授权
    Semiconductor inspection and metrology systems for distributing job among the CPUs or GPUs based on logical image processing boundaries 有权

    公开(公告)号：US11237872B2

    公开(公告)日：2022-02-01

    申请号：US15978626

    申请日：2018-05-14

    Applicant: KLA-TENCOR CORPORATION

    Inventor： Ajay Gupta ,  Sankar Venkataraman ,  Sashi Balasingam ,  Mohan Mahadevan

    IPC: G06F9/50 ,  G06F9/48

    Abstract: Real-time job distribution software architectures for high bandwidth, hybrid processor computation systems for semiconductor inspection and metrology are disclosed. The imaging processing computer architecture can be scalable by changing the number of CPUs and GPUs to meet computing needs. The architecture is defined using a master node and one or more worker nodes to run image processing jobs in parallel for maximum throughput. The master node can receive input image data from a semiconductor wafer or reticle. Jobs based on the input image data are distributed to one of the worker nodes. Each worker node can include at least one CPU and at least one GPU. The image processing job can contain multiple tasks, and each of the tasks can be assigned to one of the CPU or GPU in the worker node using a worker job manager to process the image.

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