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    Wafer Defect Discovery
    1.
    发明申请
    Wafer Defect Discovery 有权
    晶圆缺陷发现

    公开(公告)号：US20160123898A1

    公开(公告)日：2016-05-05

    申请号：US14931226

    申请日：2015-11-03

    Applicant: KLA-Tencor Corporation

    Inventor： Hong Chen ,  Kenong Wu ,  Martin Plihal ,  Vidur Pandita ,  Ravikumar Sanapala ,  Vivek Bhagat ,  Rahul Lakhawat ,  Oksen Baris ,  Rajesh Ramachandran ,  Naoshin Haque

    IPC: G01N21/95

    CPC classification number: H01J37/28 ,  G01N21/8851 ,  G01N21/9501 ,  G01N2021/8854 ,  G01N2021/8883 ,  G03F7/70625 ,  G03F7/7065 ,  H01J2237/24592

    Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

    Abstract translation: 提供了发现晶片缺陷的系统和方法。 一种方法包括通过在晶片的第一次扫描中施加由检测器产生的输出的阈值来检测晶片上的缺陷，并确定检测到的缺陷的特征的值。 该方法还包括对特征进行自动排序，识别特征切割线以将缺陷分组成箱，并且对于每个箱，确定一个或多个参数，如果应用于每个中的缺陷的特征的值 箱将导致每个箱中预定数量的缺陷。 该方法还包括将一个或多个确定的参数应用于在晶片的第二次扫描中由检测器产生的输出，以产生具有预定缺陷计数并且在特征值中多样化的缺陷群体。

    Wafer Defect Discovery
    2.
    发明申请
    Wafer Defect Discovery 审中-公开
    晶圆缺陷发现

    公开(公告)号：US20170076911A1

    公开(公告)日：2017-03-16

    申请号：US15359589

    申请日：2016-11-22

    Applicant: KLA-Tencor Corporation

    Inventor： Hong Chen ,  Kenong Wu ,  Martin Plihal ,  Vidur Pandita ,  Ravikumar Sanapala ,  Vivek Bhagat ,  Rahul Lakhawat ,  Oksen Baris ,  Rajesh Ramachandran ,  Naoshin Haque

    IPC: H01J37/28

    CPC classification number: H01J37/28 ,  G01N21/8851 ,  G01N21/9501 ,  G01N2021/8854 ,  G01N2021/8883 ,  G03F7/70625 ,  G03F7/7065 ,  H01J2237/24592

    Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

    Abstract translation: 提供了发现晶片缺陷的系统和方法。 一种方法包括通过在晶片的第一次扫描中施加由检测器产生的输出的阈值来检测晶片上的缺陷，并确定检测到的缺陷的特征的值。 该方法还包括对特征进行自动排序，识别特征切割线以将缺陷分组成箱，并且对于每个箱，确定一个或多个参数，如果应用于每个中的缺陷的特征的值 箱将导致每个箱中预定数量的缺陷。 该方法还包括将一个或多个确定的参数应用于在晶片的第二次扫描中由检测器产生的输出，以产生具有预定缺陷计数并且在特征值中多样化的缺陷群体。

    Super-resolution defect review image generation through generative adversarial networks
    3.
    发明授权
    Super-resolution defect review image generation through generative adversarial networks 有权

    公开(公告)号：US10949964B2

    公开(公告)日：2021-03-16

    申请号：US16180957

    申请日：2018-11-05

    Applicant: KLA-Tencor Corporation

    Inventor： Anuj Pandey ,  Bradley Ries ,  Himanshu Vajaria ,  Yong Zhang ,  Rahul Lakhawat

    IPC: G06T7/00 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: A system for analyzing a sample includes an inspection sub-system and at least one controller. The inspection sub-system is configured to scan a sample to collect a first plurality of sample images having a first image resolution. The controller is configured to generate a defect list based on the first plurality of sample images. The controller is further configured to input images corresponding to the defect list into a neural network that is trained with source data including sample images having the first image resolution and sample images having a second image resolution higher than the first image resolution. The controller is further configured to generate a second plurality of sample images with the neural network based on the images corresponding to the defect list, where the second plurality of sample images have the second image resolution and correspond to the defect list.

    Sub-Pixel Alignment of Inspection to Design
    5.
    发明申请
    Sub-Pixel Alignment of Inspection to Design 有权
    子像素对齐检验到设计

    公开(公告)号：US20160275672A1

    公开(公告)日：2016-09-22

    申请号：US15073617

    申请日：2016-03-17

    Applicant: KLA-Tencor Corporation

    Inventor： Santosh Bhattacharyya ,  Pavan Kumar ,  Lisheng Gao ,  Thirupurasundari Jayaraman ,  Raghav Babulnath ,  Srikanth Kandukuri ,  Gangadharan Sivaraman ,  Karthikeyan Subramanian ,  Raghavan Konuru ,  Rahul Lakhawat

    IPC: G06T7/00

    CPC classification number: G06T7/33 ,  G06T2207/10061 ,  G06T2207/30148

    Abstract: Methods and systems for determining a position of output generated by an inspection subsystem in design data space are provided. In general, some embodiments described herein are configured for substantially accurately aligning inspection subsystem output generated for a specimen to a design for the specimen despite deformation of the design in the inspection subsystem output. In addition, some embodiments are configured for generating and/or using alignment targets that can be shared across multiple specimens of the same layer and design rule for alignment of inspection subsystem output generated for a specimen to a design for the specimen.

    Abstract translation: 提供了用于确定由检查子系统在设计数据空间中产生的输出位置的方法和系统。 通常，这里描述的一些实施例被配置成用于将检测子系统产生的样品基本上精确地对准于试样的设计，尽管检查子系统输出中的设计变形。 此外，一些实施例被配置用于生成和/或使用可以在同一层的多个样本上共享的对准目标和用于将为样本生成的检查子系统输出对准样本的设计规则的设计规则。

    Super-Resolution Defect Review Image Generation Through Generative Adversarial Networks
    6.
    发明申请
    Super-Resolution Defect Review Image Generation Through Generative Adversarial Networks 审中-公开

    公开(公告)号：US20200098101A1

    公开(公告)日：2020-03-26

    申请号：US16180957

    申请日：2018-11-05

    Applicant: KLA-Tencor Corporation

    Inventor： Anuj Pandey ,  Bradley Ries ,  Himanshu Vajaria ,  Yong Zhang ,  Rahul Lakhawat

    IPC: G06T7/00 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: A system for analyzing a sample includes an inspection sub-system and at least one controller. The inspection sub-system is configured to scan a sample to collect a first plurality of sample images having a first image resolution. The controller is configured to generate a defect list based on the first plurality of sample images. The controller is further configured to input images corresponding to the defect list into a neural network that is trained with source data including sample images having the first image resolution and sample images having a second image resolution higher than the first image resolution. The controller is further configured to generate a second plurality of sample images with the neural network based on the images corresponding to the defect list, where the second plurality of sample images have the second image resolution and correspond to the defect list.

    Wafer defect discovery
    7.
    发明授权
    Wafer defect discovery 有权
    晶圆缺陷发现

    公开(公告)号：US09518934B2

    公开(公告)日：2016-12-13

    申请号：US14931226

    申请日：2015-11-03

    Applicant: KLA-Tencor Corporation

    Inventor： Hong Chen ,  Kenong Wu ,  Martin Plihal ,  Vidur Pandita ,  Ravikumar Sanapala ,  Vivek Bhagat ,  Rahul Lakhawat ,  Oksen Baris ,  Rajesh Ramachandran ,  Naoshin Haque

    IPC: G01N21/00 ,  G01N21/95 ,  G01N21/88 ,  G03F7/20

    CPC classification number: H01J37/28 ,  G01N21/8851 ,  G01N21/9501 ,  G01N2021/8854 ,  G01N2021/8883 ,  G03F7/70625 ,  G03F7/7065 ,  H01J2237/24592

    Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

    Abstract translation: 提供了发现晶片缺陷的系统和方法。 一种方法包括通过在晶片的第一次扫描中施加由检测器产生的输出的阈值来检测晶片上的缺陷，并确定检测到的缺陷的特征的值。 该方法还包括对特征进行自动排序，识别特征切割线以将缺陷分组成箱，并且对于每个箱，确定一个或多个参数，如果应用于每个中的缺陷的特征的值 箱将导致每个箱中预定数量的缺陷。 该方法还包括将一个或多个确定的参数应用于在晶片的第二次扫描中由检测器产生的输出，以产生具有预定缺陷计数并且在特征值中多样化的缺陷群体。

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