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公开(公告)号：US20120242667A1
公开(公告)日：2012-09-27
申请号：US13052943
申请日：2011-03-21
申请人： Sanjeev Kaushal , Kenji Sugishima , Sukesh Janubhai Patel , Robert Filman , Wolfgang Polak , Orion Wolfe , Jessie Burger
发明人： Sanjeev Kaushal , Kenji Sugishima , Sukesh Janubhai Patel , Robert Filman , Wolfgang Polak , Orion Wolfe , Jessie Burger
IPC分类号： G06T11/20
CPC分类号： G06N99/005 , G05B23/0237 , G06F3/04842
摘要： The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.
摘要翻译： 本公开涉及自动学习应用于任意半导体制造工具的多个制造工具参数之间的关系以及至少部分由自主学习系统支持的图形用户界面。 图形用户界面可以基于接收到的数据创建一个或多个矩阵，并且可以通过转换一个或多个矩阵来进一步生成附加矩阵。 可以输出一系列窗口，其中该系列窗口提供包括聚焦室和参考室之间的匹配的性能分析。 在一个方面，聚焦室和参考室可以是不同的室。 在另一方面，聚焦室和参考室可以是相同的室，其提供了相同室随时间的性能劣化的分析。
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公开(公告)号：US08723869B2
公开(公告)日：2014-05-13
申请号：US13052943
申请日：2011-03-21
申请人： Sanjeev Kaushal , Kenji Sugishima , Sukesh Janubhai Patel , Robert Filman , Wolfgang Polak , Orion Wolfe , Jessie Burger
发明人： Sanjeev Kaushal , Kenji Sugishima , Sukesh Janubhai Patel , Robert Filman , Wolfgang Polak , Orion Wolfe , Jessie Burger
IPC分类号： G06T11/20
CPC分类号： G06N99/005 , G05B23/0237 , G06F3/04842
摘要： The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.
摘要翻译： 本公开涉及自动学习应用于任意半导体制造工具的多个制造工具参数之间的关系以及至少部分由自主学习系统支持的图形用户界面。 图形用户界面可以基于接收到的数据创建一个或多个矩阵，并且可以通过转换一个或多个矩阵来进一步生成附加矩阵。 可以输出一系列窗口，其中该系列窗口提供包括聚焦室和参考室之间的匹配的性能分析。 在一个方面，聚焦室和参考室可以是不同的室。 在另一方面，聚焦室和参考室可以是相同的室，其提供了相同室随时间的性能劣化的分析。
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3.发明申请公开(公告)号：US20090240366A1
公开(公告)日：2009-09-24
申请号：US12416018
申请日：2009-03-31
CPC分类号： G05B13/0265 , G05B19/4065 , G05B2219/33041 , G05B2219/37252 , G06N5/04 , G06N99/005 , Y02P90/86
摘要： Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.
摘要翻译： 提供了自动生物学的学习工具系统和工具系统用于学习和分析性能下降和失配的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并且生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 可以利用从合成或生产数据中获取的客观生成的知识来确定特定输出变量与一组相关影响变量之间的数学关系。 生成的关系有助于评估一组工具的性能下降，以及其中的工具之间的性能不匹配。
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4.发明申请TOOL PERFORMANCE BY LINKING SPECTROSCOPIC INFORMATION WITH TOOL OPERATIONAL PARAMETERS AND MATERIAL MEASUREMENT INFORMATION 有权通过连接光谱信息与工具操作参数和材料测量信息的工具性能公开(公告)号：US20120185813A1
公开(公告)日：2012-07-19
申请号：US13009685
申请日：2011-01-19
IPC分类号： G06F17/50
CPC分类号： H01L22/20 , G05B19/41875 , G06N3/126 , G06N99/005 , H01J37/32926 , H01L22/12 , H01L22/26 , H01L2924/0002 , Y02P90/10 , Y02P90/20 , Y02P90/22 , H01L2924/00
摘要： System(s) and method(s) are provided for adjustment and analysis of performance of a tool through integration of tool operational data and spectroscopic data related to the tool. Such integration results in consolidated data that enable, in part, learning at least one relationship amongst selected portions of the consolidated data. Learning is performed autonomously without human intervention. Adjustment of performance of the tool relies at least in part on a learned relationship and includes generation of process recipe parameter(s) that can adjust a manufacturing process in order to produce a satisfactory tool performance in response to implementation of the manufacturing process. A process recipe parameter can be generated by solving an inverse problem based on the learned relationship. Analysis of performance of the tool can include assessment of synthetic performance scenarios, identification of spectroscopic condition(s) that affect performance, and extraction of endpoints based at least on time dependence spectroscopic data.
摘要翻译： 提供了系统和方法，用于通过集成工具操作数据和与工具相关的光谱数据来调整和分析工具的性能。 这种集成导致统一数据，部分地可以在合并数据的选定部分中学习至少一个关系。 没有人为干预，自主学习。 工具的性能调整至少部分取决于学习的关系，并且包括生成可以调整制造过程的工艺配方参数，以便响应于制造过程的实施而产生令人满意的工具性能。 可以通过基于学习关系求解逆问题来生成过程配方参数。 工具的性能分析可以包括综合性能情景的评估，影响性能的光谱条件的识别，以及至少基于时间的光谱数据提取端点。
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公开(公告)号：US20090228408A1
公开(公告)日：2009-09-10
申请号：US12044959
申请日：2008-03-08
CPC分类号： G05B13/0265 , G06N5/04 , G06N99/005
摘要： An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Similarly, the one or more tools can be deployed recursively, in a bottom-up manner in which an individual autonomous tools is assembled in conjunction with other (disparate or alike) autonomous tools to form an autonomous group tool, which in turn can be assembled with other group tools to form a conglomerated autonomous tool system. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual, group and conglomerated tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.
摘要翻译： 提供了一种自主的基于生物学的学习工具系统和工具系统用于学习和分析的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 自主学习系统包括通过网络进行通信的存储器平台和处理平台。 该网络通过交互管理器从工具系统和外部演员接收数据。 存储器平台和处理平台都包括可以递归定义的功能组件和存储器。 类似地，可以以自下而上的方式递归地部署一个或多个工具，其中单独的自主工具与其他（不同或相似）的自主工具结合组合以形成自主的组工具，其又可以被组装 与其他团体工具组成一个集团自主的工具系统。 在与个人，团体和集体工具相关的自主学习系统中生成和积累的知识可以被投入到可以用于基于上下文学习和驱动工具目标的语义网络中。
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6.发明授权Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool 有权用于自学习和自我改进的半导体制造工具的方法和装置公开(公告)号：US08396582B2
公开(公告)日：2013-03-12
申请号：US12697121
申请日：2010-01-29
CPC分类号： G06N99/005 , G05B13/0265 , G06N5/04
摘要： System(s) and method(s) for optimizing performance of a manufacturing tool are provided. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.
摘要翻译： 提供了用于优化制造工具性能的系统和方法。 优化依赖于食谱漂移和产生知识，捕获产品输出指标和输入材料测量和配方参数之间的关系。 从学习功能的基础提取优化的配方参数，该函数预测制造工具的当前状态的输出度量和输入材料的测量。 漂移和学习相关，导致刀具性能的动态优化，从而随着刀具的操作条件的变化，可以优化制造工具的输出。 配方漂移和相关学习的特征可以通过适当的用户界面进行自主或外部配置，也可以通过漂移来优化最终用户交互。
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公开(公告)号：US20110131162A1
公开(公告)日：2011-06-02
申请号：US12044958
申请日：2008-03-08
CPC分类号： G06N5/04 , G05B13/0265 , G06N5/02 , G06N99/005
摘要： An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Similarly, the one or more tools can be deployed recursively, in a bottom-up manner in which an individual autonomous tools is assembled in conjunction with other (disparate or alike) autonomous tools to form an autonomous group tool, which in turn can be assembled with other group tools to form a conglomerated autonomous tool system. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual, group and conglomerated tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.
摘要翻译： 提供了一种自主的基于生物学的学习工具系统和工具系统用于学习和分析的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 自主学习系统包括通过网络进行通信的存储器平台和处理平台。 该网络通过交互管理器从工具系统和外部演员接收数据。 存储器平台和处理平台都包括可以递归定义的功能组件和存储器。 类似地，可以以自下而上的方式递归地部署一个或多个工具，其中单独的自主工具与其他（不同或相似）的自主工具结合组合以形成自主的组工具，其又可以被组装 与其他团体工具组成一个集团自主的工具系统。 在与个人，团体和集体工具相关的自主学习系统中生成和积累的知识可以被投入到可以用于基于上下文学习和驱动工具目标的语义网络中。
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8.发明授权公开(公告)号：US08725667B2
公开(公告)日：2014-05-13
申请号：US12416018
申请日：2009-03-31
CPC分类号： G05B13/0265 , G05B19/4065 , G05B2219/33041 , G05B2219/37252 , G06N5/04 , G06N99/005 , Y02P90/86
摘要： Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.
摘要翻译： 提供了自动生物学的学习工具系统和工具系统用于学习和分析性能下降和失配的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 可以利用从合成或生产数据中获取的客观生成的知识来确定特定输出变量与一组相关影响变量之间的数学关系。 生成的关系有助于评估一组工具的性能下降，以及其中的工具之间的性能不匹配。
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公开(公告)号：US20120209798A1
公开(公告)日：2012-08-16
申请号：US13457830
申请日：2012-04-27
IPC分类号： G06N3/12
CPC分类号： G06N5/04 , G05B13/0265 , G06N5/02 , G06N99/005
摘要： An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.
摘要翻译： 提供了一种自主的基于生物学的学习工具系统和工具系统用于学习和分析的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 自主学习系统包括通过网络进行通信的存储器平台和处理平台。 存储器平台和处理平台都包括可以递归定义的功能组件和存储器。 在自主学习系统中生成和积累的知识可以被投入到可以用于基于上下文学习和驱动工具目标的语义网络中。
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公开(公告)号：US09275335B2
公开(公告)日：2016-03-01
申请号：US13457830
申请日：2012-04-27
CPC分类号： G06N5/04 , G05B13/0265 , G06N5/02 , G06N99/005
摘要： An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.
摘要翻译： 提供了一种自主的基于生物学的学习工具系统和工具系统用于学习和分析的方法。 自主的基于生物学的学习工具系统包括（a）执行一组特定任务或过程的一个或多个工具系统，并生成与表征各种过程和相关工具性能的资产相关的资产和数据; （b）接收和格式化数据的交互管理器，（c）基于生物学习原理的自主学习系统。 自主学习系统包括通过网络进行通信的存储器平台和处理平台。 存储器平台和处理平台都包括可以递归定义的功能组件和存储器。 在自主学习系统中生成和积累的知识可以被投入到可以用于基于上下文学习和驱动工具目标的语义网络中。
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