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1.发明申请METHODS AND SYSTEMS FOR APPLYING RUN-TO-RUN CONTROL AND VIRTUAL METROLOGY TO REDUCE EQUIPMENT RECOVERY TIME 审中-公开用于运行运行控制和虚拟计量的方法和系统，以减少设备恢复时间公开(公告)号：US20160342147A1
公开(公告)日：2016-11-24
申请号：US14716838
申请日：2015-05-19
Applicant: Applied Materials, Inc.
Inventor： Jimmy Iskandar , Jianping Zou , Parris C.M. Hawkins , James Moyne
IPC: G05B19/402 , G05B19/404
CPC classification number: G05B23/0294 , G05B23/024
Abstract: Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting, with a system, data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility and determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data. The method further includes utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool. Applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time.
Abstract translation: 这里描述了用于减少设备维修时间的方法，装置和系统。 在一个实施例中，计算机实现的方法包括与系统一起收集包括测试基板数据或其他测量数据和故障检测数据的数据，用于在制造设备中维护至少一个制造工具的恢复，并且与系统确定关系 在至少一个制造工具的工具参数设置和包括测试衬底数据的至少一些收集的数据之间。 该方法还包括利用零个或多个虚拟测量预测算法和至少一些收集的数据来获得度量预测和应用多变量运行（R2R）控制建模，以获得包括至少至少的当前操作区域的状态估计 一个基于测试基板数据的制造工具，并为至少一个制造工具的至少一个目标参数获得至少一个刀具参数调整。 应用多变量运行（R2R）控制建模以获得至少一个制造工具的工具参数调整，在维护后发生，以减少维护恢复时间并减少重新定标时间。
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公开(公告)号：US11126172B2
公开(公告)日：2021-09-21
申请号：US16538689
申请日：2019-08-12
Applicant: Applied Materials, Inc.
Inventor： Jimmy Iskandar , Jianping Zou , Parris C. M. Hawkins , James Moyne
IPC: G05B23/02
Abstract: Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility and determining a relationship between tool parameter settings for the manufacturing tool and the test substrate data. The method further includes utilizing virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool. Applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool to reduce maintenance recovery time and to reduce requalification time.
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Patent Agency Ranking
公开(公告)号：US11022968B2
公开(公告)日：2021-06-01
申请号：US16538676
申请日：2019-08-12
Applicant: Applied Materials, Inc.
Inventor： Jimmy Iskandar , Jianping Zou , Parris C. M. Hawkins , James Moyne
IPC: G05B23/02
Abstract: Described herein are methods, apparatuses, and systems for reducing equipment repair time. Disclosed methods include collecting data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility. Disclosed methods include determining a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data. The disclosure includes utilizing virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a tool parameter adjustment for at least one target parameter for the at least one manufacturing tool. The disclosure further includes applying the R2R control modeling to obtain tool parameter adjustments for at least one manufacturing tool.
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公开(公告)号：US10409231B2
公开(公告)日：2019-09-10
申请号：US15889906
申请日：2018-02-06
Applicant: Applied Materials, Inc.
Inventor： James Moyne
Abstract: Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility.
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公开(公告)号：US20140006338A1
公开(公告)日：2014-01-02
申请号：US13929615
申请日：2013-06-27
Applicant: Applied Materials, Inc.
Inventor： Scott Watson , Jamini Samantaray , John Scoville , James Moyne
IPC: G06F17/30
CPC classification number: G06F16/254
Abstract: A big data analytics system obtains a plurality of manufacturing parameters associated with a manufacturing facility. The big data analytics system identifies first real-time data from a plurality of data sources to store in memory-resident storage based on the plurality of manufacturing parameters. The plurality of data sources are associated with the manufacturing facility. The big data analytics system obtains second real-time data from the plurality of data sources to store in distributed storage based on the plurality of manufacturing parameters.
Abstract translation: 大数据分析系统获得与制造设备相关联的多个制造参数。 大数据分析系统基于多个制造参数识别来自多个数据源的第一实时数据以存储在存储器驻留的存储器中。 多个数据源与制造设备相关联。 大数据分析系统基于多个制造参数从多个数据源获得第二实时数据以存储在分布式存储器中。
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6.发明申请INCREASING SIGNAL TO NOISE RATIO FOR CREATION OF GENERALIZED AND ROBUST PREDICTION MODELS 有权增加噪声信号以创建广义和可靠的预测模型公开(公告)号：US20130268469A1
公开(公告)日：2013-10-10
申请号：US13856288
申请日：2013-04-03
Applicant: APPLIED MATERIALS, INC.
Inventor： Deepak Sharma , Helen R. Armer , James Moyne
IPC: G06N5/02
CPC classification number: G06N5/02 , G05B19/00 , G06F17/5036 , G06N5/022 , G06N99/005
Abstract: A computer system iteratively executes a decision tree-based prediction model using a set of input variables. The iterations create corresponding rankings of the input variables. The computer system generates overall variables contribution data using the rankings of the input variables and identifies key input variables based on the overall variables contribution data.
Abstract translation: 计算机系统使用一组输入变量迭代地执行基于决策树的预测模型。 迭代创建输入变量的相应排名。 计算机系统使用输入变量的排名生成总体变量贡献数据，并根据总变量贡献数据识别关键输入变量。
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7.发明授权公开(公告)号：US09275334B2
公开(公告)日：2016-03-01
申请号：US13856288
申请日：2013-04-03
Applicant: Applied Materials, Inc.
Inventor： Deepak Sharma , Helen R. Armer , James Moyne
CPC classification number: G06N5/02 , G05B19/00 , G06F17/5036 , G06N5/022 , G06N99/005
Abstract: A computer system iteratively executes a decision tree-based prediction model using a set of input variables. The iterations create corresponding rankings of the input variables. The computer system generates overall variables contribution data using the rankings of the input variables and identifies key input variables based on the overall variables contribution data.
Abstract translation: 计算机系统使用一组输入变量迭代地执行基于决策树的预测模型。 迭代创建输入变量的相应排名。 计算机系统使用输入变量的排名生成总体变量贡献数据，并根据总变量贡献数据识别关键输入变量。
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