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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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32.发明授权Tool performance by linking spectroscopic information with tool operational parameters and material measurement information 有权通过将光谱信息与工具操作参数和材料测量信息相关联来实现工具性能公开(公告)号：US08954184B2
公开(公告)日：2015-02-10
申请号：US13009685
申请日：2011-01-19
IPC分类号： G06F19/00 , G01D18/00 , H01L21/66 , G05B19/418 , H01J37/32
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：US08190543B2
公开(公告)日：2012-05-29
申请号：US12044958
申请日：2008-03-08
IPC分类号： G06N5/00
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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34.发明授权Autonomous adaptive system and method for improving semiconductor manufacturing quality 有权自适应系统和提高半导体制造质量的方法公开(公告)号：US08078552B2
公开(公告)日：2011-12-13
申请号：US12044959
申请日：2008-03-08
IPC分类号： G06F15/18
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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35.发明申请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半导体制造工具的方法和装置公开(公告)号：US20100138026A1
公开(公告)日：2010-06-03
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：US07561269B2
公开(公告)日：2009-07-14
申请号：US11956751
申请日：2007-12-14
CPC分类号： G01N21/95607
摘要： An optical measurement system and wafer processing tool for correcting systematic errors in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.
摘要翻译： 一种用于校正系统误差的光学测量系统和晶片处理工具，其中通过将标准衬底暴露于电磁能谱，从包括具有已知折射率和已知消光系数的层的标准衬底测量第一衍射光谱。 计算标准底物的工具完美衍射光谱。 通过将测量的衍射光谱与计算的工具完美衍射光谱进行比较来计算硬件系统误差。 通过将工件暴露于电磁能的频谱来测量来自工件的第二衍射光谱，并且基于所计算的硬件系统误差来校正所测量的第二衍射光谱，以获得校正的衍射光谱。
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公开(公告)号：US07342244B2
公开(公告)日：2008-03-11
申请号：US11488752
申请日：2006-07-19
CPC分类号： H01L29/66984 , H01F10/16 , H01L29/1054
摘要： A semiconductor device including: a substrate comprising silicon; a channel region formed on the substrate; a spin injector formed on the substrate at a first side of the channel region and configured to diffuse a spin-polarized current into the channel region; a spin detector formed on the substrate at a second side of the channel region and configured to receive the spin polarized current from the channel region; and a gate formed on the substrate in an area of the channel region.
摘要翻译： 一种半导体器件，包括：包含硅的衬底; 形成在所述基板上的沟道区域; 旋转注入器，其形成在所述沟道区域的第一侧的所述衬底上并且被配置为将自旋极化电流扩散到所述沟道区域中; 旋转检测器，形成在所述沟道区的第二侧的所述衬底上，并被配置为从所述沟道区接收所述自旋极化电流; 以及形成在沟道区域的区域中的衬底上的栅极。
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38.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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公开(公告)号：US20090153842A1
公开(公告)日：2009-06-18
申请号：US11956751
申请日：2007-12-14
IPC分类号： G01N21/00
CPC分类号： G01N21/95607
摘要： An optical measurement system and wafer processing tool for correcting systematic errors in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.
摘要翻译： 一种用于校正系统误差的光学测量系统和晶片处理工具，其中通过将标准衬底暴露于电磁能谱，从包括具有已知折射率和已知消光系数的层的标准衬底测量第一衍射光谱。 计算标准底物的工具完美衍射光谱。 通过将测量的衍射光谱与计算的工具完美衍射光谱进行比较来计算硬件系统误差。 通过将工件暴露于电磁能的频谱来测量来自工件的第二衍射光谱，并且基于所计算的硬件系统误差来校正所测量的第二衍射光谱，以获得校正的衍射光谱。
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