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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：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：US10181185B2
公开(公告)日：2019-01-15
申请号：US15402197
申请日：2017-01-09
Applicant: KLA-Tencor Corporation
Inventor： Allen Park , Lisheng Gao , Ashok Kulkarni , Saibal Banerjee , Ping Gu , Songnian Rong , Kris Bhaskar
Abstract: Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.
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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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Patent Agency Ranking
公开(公告)号：US20130096873A1
公开(公告)日：2013-04-18
申请号：US13652232
申请日：2012-10-15
Applicant: KLA-Tencor Corporation
Inventor： Eliezer Rosengaus , Ady Levy , Kris Bhaskar
CPC classification number: G01C15/002
Abstract: Systems and methods for acquiring information for a construction site are provided. One system includes a base unit positioned within a construction site by a user. A computer subsystem of the base unit determines a position of the base unit with respect to the construction site. The system also includes a measurement unit moved within the construction site by a user. The measurement unit includes one or more elements configured to interact with light in a known manner. An optical subsystem of the base unit directs light to the element(s) and detects the light after interacting with the element(s). The computer subsystem is configured to determine a position and pose of the measurement unit with respect to the base unit based on the detected light. The measurement unit includes a measurement device used by the measurement unit or the base unit to determine information for the construction site.
Abstract translation: 提供了获取施工现场信息的系统和方法。 一个系统包括由用户定位在施工现场内的基座单元。 基座的计算机子系统确定基座相对于施工现场的位置。 该系统还包括由用户在施工现场内移动的测量单元。 测量单元包括被配置为以已知方式与光相互作用的一个或多个元件。 基本单元的光学子系统将光引导到元件，并在与元件相互作用之后检测光。 计算机子系统被配置为基于检测到的光来确定测量单元相对于基本单元的位置和姿态。 测量单元包括由测量单元或基座单元用于确定施工现场的信息的测量装置。
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7.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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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：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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10.发明申请公开(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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