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1.发明授权公开(公告)号：US11580398B2
公开(公告)日：2023-02-14
申请号：US15694719
申请日：2017-09-01
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Ravi Chandra Donapati , Mark Roulo , Kris Bhaskar
Abstract: Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.
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公开(公告)号：US10416087B2
公开(公告)日：2019-09-17
申请号：US15357888
申请日：2016-11-21
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Jeremy Nesbitt , Grace Hsiu-Ling Chen , Richard Wallingford
Abstract: An inspection system includes an illumination sub-system, a collection sub-system, and a controller. The illumination sub-system includes an illumination source configured to generate a beam of illumination and a set of illumination optics to direct the beam of illumination to a sample. The collection sub-system includes a set of collection optics to collect illumination emanating from the sample and a detector configured to receive the collected illumination from the sample. The controller is configured to acquire a test image of the sample, reconstruct the test image to enhance the resolution of the test image, and detect one or more defects on the sample based on the reconstructed test image.
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公开(公告)号：US20170345140A1
公开(公告)日：2017-11-30
申请号：US15603249
申请日：2017-05-23
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Kris Bhaskar
IPC: G06T7/00
CPC classification number: G06T7/0004 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: Methods and systems for generating a simulated image from an input image are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a neural network that includes two or more encoder layers configured for determining features of an image for a specimen. The neural network also includes two or more decoder layers configured for generating one or more simulated images from the determined features. The neural network does not include a fully connected layer thereby eliminating constraints on size of the image input to the two or more encoder layers.
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公开(公告)号：US20170148226A1
公开(公告)日：2017-05-25
申请号：US15176139
申请日：2016-06-07
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Kris Bhaskar
CPC classification number: G06T19/20 , G06F17/5081 , G06K9/4628 , G06K9/6257 , G06T7/00 , G06T7/001 , G06T9/00 , G06T2200/08 , G06T2207/10061 , G06T2207/30148
Abstract: Methods and systems for generating simulated images from design information are provided. One system includes one or more computer subsystems and one or more components executed by the computer subsystem(s), which include a generative model. The generative model includes two or more encoder layers configured for determining features of design information for a specimen. The generative model also includes two or more decoder layers configured for generating one or more simulated images from the determined features. The simulated image(s) illustrate how the design information formed on the specimen appears in one or more actual images of the specimen generated by an imaging system.
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5.发明授权公开(公告)号：US10648924B2
公开(公告)日：2020-05-12
申请号：US15396800
申请日：2017-01-02
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Grace Hsiu-Ling Chen , Kris Bhaskar , Keith Wells , Nan Bai , Ping Gu , Lisheng Gao
Abstract: Methods and systems for generating a high resolution image for a specimen from one or more low resolution images of the specimen are provided. One system includes one or more computer subsystems configured for acquiring one or more low resolution images of a specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a model that includes one or more first layers configured for generating a representation of the one or more low resolution images. The model also includes one or more second layers configured for generating a high resolution image of the specimen from the representation of the one or more low resolution images.
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Patent Agency Ranking
公开(公告)号：US20170194126A1
公开(公告)日：2017-07-06
申请号：US15394792
申请日：2016-12-29
Applicant: KLA-Tencor Corporation
Inventor： Kris Bhaskar , Grace Hsiu-Ling Chen , Keith Wells , Wayne McMillan , Jing Zhang , Scott Young , Brian Duffy
IPC: H01J37/22 , G01N23/225 , G01N21/95 , H01J37/06 , H01J37/28
CPC classification number: H01J37/222 , G01N21/9501 , G01N23/2251 , G01N2201/12 , G01N2223/304 , G01N2223/401 , G01N2223/418 , G01N2223/6116 , G01N2223/646 , G03F7/7065 , H01J37/06 , H01J37/226 , H01J37/28 , H01J2237/24475 , H01J2237/24495 , H01J2237/2817 , H01L22/20
Abstract: Hybrid inspectors are provided. One system includes computer subsystems) configured for receiving optical based output and electron beam based output generated for a specimen. The computer subsystem(s) include one or more virtual systems configured for performing one or more functions using at least some of the optical based output and the electron beam based output generated for the specimen. The system also includes one or more components executed by the computer subsystem(s), which include one or more models configured for performing one or more simulations for the specimen. The computer subsystem(s) are configured for detecting defects on the specimen based on at least two of the optical based output, the electron beam based output, results of the one or more functions, and results of the one or more simulations.
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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：US10713769B2
公开(公告)日：2020-07-14
申请号：US16424431
申请日：2019-05-28
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Yujie Dong , Brian Duffy , Richard Wallingford , Michael Daino , Kris Bhaskar
Abstract: Methods and systems for performing active learning for defect classifiers are provided. One system includes one or more computer subsystems configured for performing active learning for training a defect classifier. The active learning includes applying an acquisition function to data points for the specimen. The acquisition function selects one or more of the data points based on uncertainty estimations associated with the data points. The active learning also includes acquiring labels for the selected one or more data points and generating a set of labeled data that includes the selected one or more data points and the acquired labels. The computer subsystem(s) are also configured for training the defect classifier using the set of labeled data. The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem.
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公开(公告)号：US20170200260A1
公开(公告)日：2017-07-13
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
CPC classification number: G06K9/6256 , G06K9/4628 , G06K9/6271 , G06K2209/19 , G06T7/0004 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
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：US10395356B2
公开(公告)日：2019-08-27
申请号：US15603249
申请日：2017-05-23
Applicant: KLA-Tencor Corporation
Inventor： Jing Zhang , Kris Bhaskar
IPC: G06T7/00
Abstract: Methods and systems for generating a simulated image from an input image are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a neural network that includes two or more encoder layers configured for determining features of an image for a specimen. The neural network also includes two or more decoder layers configured for generating one or more simulated images from the determined features. The neural network does not include a fully connected layer thereby eliminating constraints on size of the image input to the two or more encoder layers.
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