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    TRAINING A MACHINE LEARNING MODEL WITH SYNTHETIC IMAGES
    12.
    发明申请
    TRAINING A MACHINE LEARNING MODEL WITH SYNTHETIC IMAGES 审中-公开

    公开(公告)号：US20190294923A1

    公开(公告)日：2019-09-26

    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: G06K9/62 ,  G06N20/00 ,  G06F3/0482 ,  G06T7/00

    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.

    Adaptive automatic defect classification
    13.
    发明授权
    Adaptive automatic defect classification 有权

    公开(公告)号：US10436720B2

    公开(公告)日：2019-10-08

    申请号：US14991901

    申请日：2016-01-08

    Applicant: KLA-Tencor Corporation

    Inventor： Li He ,  Martin Plihal ,  Huajun Ying ,  Anadi Bhatia ,  Amitoz Singh Dandiana ,  Ramakanth Ramini

    IPC: G01N21/95 ,  G06N20/00 ,  G01N21/88 ,  H01L21/66

    Abstract: Methods and systems for classifying defects detected on a specimen with an adaptive automatic defect classifier are provided. One method includes creating a defect classifier based on classifications received from a user for different groups of defects in first lot results and a training set of defects that includes all the defects in the first lot results. The first and additional lot results are combined to create cumulative lot results. Defects in the cumulative lot results are classified with the created defect classifier. If any of the defects are classified with a confidence below a threshold, the defect classifier is modified based on a modified training set that includes the low confidence classified defects and classifications for these defects received from a user. The modified defect classifier is then used to classify defects in additional cumulative lot results.

    Method and System for Defect Classification
    14.
    发明申请
    Method and System for Defect Classification 审中-公开

    公开(公告)号：US20180114310A1

    公开(公告)日：2018-04-26

    申请号：US15839690

    申请日：2017-12-12

    Applicant: KLA-Tencor Corporation

    Inventor： Li He ,  Chien-Huei Adam Chen ,  Sankar Venkataraman ,  John R. Jordan ,  Huajun Ying ,  Sinha Harsh

    IPC: G06T7/00 ,  G06K9/62

    CPC classification number: G06T7/0004 ,  G06K9/6256 ,  G06K9/6292 ,  G06K2009/6295 ,  G06T2207/20081 ,  G06T2207/30148

    Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

    Method and System for Iterative Defect Classification
    16.
    发明申请
    Method and System for Iterative Defect Classification 有权
    迭代缺陷分类方法与系统

    公开(公告)号：US20160358041A1

    公开(公告)日：2016-12-08

    申请号：US15010887

    申请日：2016-01-29

    Applicant: KLA-Tencor Corporation

    Inventor： Sankar Venkataraman ,  Li He ,  John R. Jordan, III ,  Oksen Baris ,  Harsh Sinha

    IPC: G06K9/62 ,  G06T7/00

    CPC classification number: G06K9/6254 ,  G06K9/6255 ,  G06K9/6269 ,  G06K9/628 ,  G06K9/6282 ,  G06T7/0004 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/30148 ,  H01L22/00 ,  H01L22/12 ,  H01L22/20

    Abstract: Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

    Abstract translation: 缺陷分类包括获取包含多个缺陷的样本的一个或多个图像，基于缺陷的属性将缺陷分组成缺陷类型组;从用户界面装置接收指示所选择数量的缺陷的第一手动分类的信号 根据第一手工分类和缺陷属性生成分类器，用分类器分类一个或多个缺乏手动分类手段分类的缺陷，识别由具有最低置信度的分类器分类的缺陷 从所述用户界面设备接收指示对具有最低置信水平的所述缺陷进行额外手动分类的信号，确定所述附加手动分类是否识别在所述第一手动分类中未识别的一个或多个附加缺陷类型，以及迭代所述程序 直到找不到新的缺陷类型。

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