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公开(公告)号：US10599951B2
公开(公告)日：2020-03-24
申请号：US16364140
申请日：2019-03-25
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Laurent Karsenti , Brad Ries , Lena Nicolaides , Richard (Seng Wee) Yeoh , Stephen Hiebert
IPC: G06K9/62
Abstract: Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.
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公开(公告)号：US09183624B2
公开(公告)日：2015-11-10
申请号：US14303602
申请日：2014-06-13
Applicant: KLA-Tencor Corporation
Inventor： Laurent Karsenti , Brian Duffy
CPC classification number: G06T7/001 , G06T2207/10061 , G06T2207/30148
Abstract: Methods and systems for detecting defects on a wafer are provided. One method includes creating a searchable database for a design for a wafer, which includes assigning values to different portions of the design based on patterns in the different portions of the design and storing the assigned values in the searchable database. Different portions of the design having substantially the same patterns are assigned the same values in the searchable database. The searchable database is configured such that searching of the database can be synchronized with generation of output for the wafer by one or more detectors of a wafer inspection system. Therefore, as the wafer is being scanned, design information for the output can be determined as fast as the output is generated, which enables multiple, desirable design based inspection capabilities.
Abstract translation: 提供了用于检测晶片上的缺陷的方法和系统。 一种方法包括创建用于晶片设计的可搜索数据库，其包括基于设计的不同部分中的图案将值设计到设计的不同部分，并将所分配的值存储在可搜索的数据库中。 具有基本上相同图案的设计的不同部分在可搜索数据库中被分配相同的值。 可搜索数据库被配置为使得可以通过晶片检查系统的一个或多个检测器与数据库的搜索同步生成晶片的输出。 因此，当正在扫描晶片时，可以确定输出的设计信息与产生的输出一样快，这样可以实现多种基于设计的检测能力。
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公开(公告)号：US20190303717A1
公开(公告)日：2019-10-03
申请号：US16364140
申请日：2019-03-25
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Laurent Karsenti , Brad Ries , Lena Nicolaides , Richard (Seng Wee) Yeoh , Stephen Hiebert
IPC: G06K9/62
Abstract: Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.
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公开(公告)号：US20170193400A1
公开(公告)日：2017-07-06
申请号：US15394790
申请日：2016-12-29
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Laurent Karsenti , Scott Young , Mohan Mahadevan , Jing Zhang , Brian Duffy , Li He , Huajun Ying , Hung Nien , Sankar Venkataraman
Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.
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公开(公告)号：US20170140524A1
公开(公告)日：2017-05-18
申请号：US15353210
申请日：2016-11-16
Applicant: KLA-Tencor Corporation
Inventor： Laurent Karsenti , Kris Bhaskar , John Raymond Jordan, III , Sankar Venkataraman , Yair Carmon
CPC classification number: G06T7/0004 , G06K9/4642 , G06K9/6256 , G06K9/6269 , G06K9/6287 , G06T2207/10061 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.
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Patent Agency Ranking
公开(公告)号：US10535131B2
公开(公告)日：2020-01-14
申请号：US15350632
申请日：2016-11-14
Applicant: KLA-Tencor Corporation
Inventor： Christopher Maher , Bjorn Brauer , Vijayakumar Ramachandran , Laurent Karsenti , Eliezer Rosengaus , John R. Jordan, III , Roni Miller
Abstract: A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.
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公开(公告)号：US10360477B2
公开(公告)日：2019-07-23
申请号：US15402169
申请日：2017-01-09
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Scott Young , Mark Roulo , Jing Zhang , Laurent Karsenti , Mohan Mahadevan , Bjorn Brauer
Abstract: Methods and systems for performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.
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公开(公告)号：US20180293721A1
公开(公告)日：2018-10-11
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 , G01N21/956 , G01N21/88
CPC classification number: G06T7/001 , G01N21/8851 , G01N21/9501 , G01N21/956 , G01N21/95607 , G01N2021/8867 , G01N2021/8887 , G05B19/41875 , G06N20/00 , G06T7/564 , G06T2207/10061 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148 , H01L22/12 , H01L22/20
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.
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公开(公告)号：US10043261B2
公开(公告)日：2018-08-07
申请号：US15402094
申请日：2017-01-09
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Jing Zhang , Grace Hsiu-Ling Chen , Ashok Kulkarni , Laurent Karsenti
Abstract: Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.
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公开(公告)号：US10186026B2
公开(公告)日：2019-01-22
申请号：US15353210
申请日：2016-11-16
Applicant: KLA-Tencor Corporation
Inventor： Laurent Karsenti , Kris Bhaskar , John Raymond Jordan, III , Sankar Venkataraman , Yair Carmon
Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.
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