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11.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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12.发明授权公开(公告)号：US09640449B2
公开(公告)日：2017-05-02
申请号：US14688739
申请日：2015-04-16
Applicant: KLA-Tencor Corporation
Inventor： Timothy Goodwin , Lena Nicolaides , Mohan Mahadevan , Paul Horn , Shifang Li
CPC classification number: H01L22/24 , G01B11/16 , G01N21/8806 , G01N21/95 , G01N21/9503 , G01N2201/06113 , H01L22/12
Abstract: Photoreflectance spectroscopy is used to measure strain at or near the edge of a wafer in a production process. The strain measurement is used to anticipate defects and make prospective corrections in later stages of the production process. Strain measurements are used to associate various production steps with defects to enhance later production processes.
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13.发明申请Automated Inline Inspection of Wafer Edge Strain Profiles Using Rapid Photoreflectance Spectroscopy 有权使用快速光反射光谱法进行晶圆边缘应变分布的自动在线检测公开(公告)号：US20150371910A1
公开(公告)日：2015-12-24
申请号：US14688739
申请日：2015-04-16
Applicant: KLA-Tencor Corporation
Inventor： Timothy Goodwin , Lena Nicolaides , Mohan Mahadevan , Paul Horn , Shifang Li
CPC classification number: H01L22/24 , G01B11/16 , G01N21/8806 , G01N21/95 , G01N21/9503 , G01N2201/06113 , H01L22/12
Abstract: Photoreflectance spectroscopy is used to measure strain at or near the edge of a wafer in a production process. The strain measurement is used to anticipate defects and make prospective corrections in later stages of the production process. Strain measurements are used to associate various production steps with defects to enhance later production processes.
Abstract translation: 光反射光谱法用于在生产过程中测量晶片边缘处或附近的应变。 应变测量用于预测缺陷，并在生产过程的后期进行前瞻性更正。 应变测量用于将各种生产步骤与缺陷相关联，以增强后续生产过程。
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公开(公告)号：US20180202943A1
公开(公告)日：2018-07-19
申请号：US15865130
申请日：2018-01-08
Applicant: KLA-Tencor Corporation
Inventor： Lu Chen , Jason Kirkwood , Mohan Mahadevan , James A. Smith , Lisheng Gao , Junqing (Jenny) Huang , Tao Luo , Richard Wallingford
CPC classification number: G01N21/9501 , G01N2021/887 , H01L22/12
Abstract: Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.
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公开(公告)号：US09880107B2
公开(公告)日：2018-01-30
申请号：US13900465
申请日：2013-05-22
Applicant: KLA-Tencor Corporation
Inventor： Lu Chen , Jason Kirkwood , Mohan Mahadevan , James A. Smith , Lisheng Gao , Junqing (Jenny) Huang , Tao Luo , Richard Wallingford
CPC classification number: G01N21/9501 , G01N2021/887 , H01L22/12
Abstract: Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.
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Patent Agency Ranking
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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17.发明申请Wafer and Lot Based Hierarchical Method Combining Customized Metrics with a Global Classification Methodology to Monitor Process Tool Condition at Extremely High Throughput 审中-公开基于晶圆和批次的分层方法将定制度量与全球分类方法相结合，以极高的吞吐量监测过程工具条件公开(公告)号：US20150234379A1
公开(公告)日：2015-08-20
申请号：US14209198
申请日：2014-03-13
Applicant: KLA-Tencor Corporation
Inventor： Himanshu Vajaria , Tommaso Torelli , Bradley Ries , Mohan Mahadevan
IPC: G05B19/418 , H01L21/02
CPC classification number: G06T7/0004 , G06T2207/20076 , G06T2207/30148 , H01L22/12 , H01L22/20 , Y02P90/14
Abstract: Methods and systems for monitoring process tool conditions are disclosed. The method combines single wafer, multiple wafers within a single lot and multiple lot information together statistically as input to a custom classification engine that can consume single or multiple scan, channel, wafer and lot to determine process tool status.
Abstract translation: 公开了用于监测工艺工具条件的方法和系统。 该方法将单个晶圆，多个晶圆和多个批次信息统一组合在一起，作为自定义分类引擎的输入，可以消耗单个或多个扫描，通道，晶圆和批次来确定过程工具状态。
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公开(公告)号：US11580375B2
公开(公告)日：2023-02-14
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：US10607119B2
公开(公告)日：2020-03-31
申请号：US15697426
申请日：2017-09-06
Applicant: KLA-Tencor Corporation
Inventor： Li He , Mohan Mahadevan , Sankar Venkataraman , Huajun Ying , Hedong Yang
Abstract: Methods and systems for detecting and classifying defects on a specimen are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for detecting defects on a specimen and classifying the defects detected on the specimen. The neural network includes a first portion configured for determining features of images of the specimen generated by an imaging subsystem. The neural network also includes a second portion configured for detecting defects on the specimen based on the determined features of the images and classifying the defects detected on the specimen based on the determined features of the images.
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公开(公告)号：US10395362B2
公开(公告)日：2019-08-27
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 , G01N21/88 , G01N21/956
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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