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1. 发明申请

WO2012129249A2 BIOLOGICALLY BASED CHAMBER MATCHING 审中-公开
标题翻译：基于生物学的腔室匹配
公开(公告)号：WO2012129249A2
公开(公告)日：2012-09-27
申请号：PCT/US2012/029847
申请日：2012-03-20
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , SUGISHIMA, Kenji , PATEL, Sukesh Janubhai , FILMAN, Robert , POLAK, Wolfgang , WOLFE, Orion , BURGER, Jessie
发明人： KAUSHAL, Sanjeev , SUGISHIMA, Kenji , PATEL, Sukesh Janubhai , FILMAN, Robert , POLAK, Wolfgang , WOLFE, Orion , BURGER, Jessie
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。

2. 发明申请

WO2009114387A1 AUTONOMOUS BIOLOGICALLY BASED LEARNING TOOL 审中-公开
标题翻译：自主生物学的学习工具
公开(公告)号：WO2009114387A1
公开(公告)日：2009-09-17
申请号：PCT/US2009/036169
申请日：2009-03-05
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
IPC分类号： G06F17/00
CPC分类号： G06N5/04 , G05B13/0265 , G06N5/02 , G06N99/005
摘要： Autonomous biologically based learning tool systems and methods that the tool systems employ for learning and analysis are provided. An 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 data, and (c) an autonomous learning system based on biological principles of learning, such system comprises a memory platform and a processing platform that are defined recursively and communicate through a network. Autonomous tool systems can be deployed recursively to assemble increasingly complex autonomous tools. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual or assembled complex autonomous tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.
摘要翻译：提供了自动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。一种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，以及（c）基于生物学习原理的自主学习系统，该系统包括递归定义并通过网络进行通信的存储器平台和处理平台。自动工具系统可以递归部署，以组合日益复杂的自主工具。在与个人或组装的复杂自主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。

3. 发明申请

WO2011094592A1 METHOD AND SYSTEM FOR SELF-LEARNING AND SELF-IMPROVING A SEMICONDUCTOR MANUFACTURING TOOL 审中-公开
标题翻译：用于自学习和自我改进的半导体制造工具的方法和系统
公开(公告)号：WO2011094592A1
公开(公告)日：2011-08-04
申请号：PCT/US2011/022997
申请日：2011-01-28
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
IPC分类号： G06F19/00
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。

4. 发明申请

WO2012099907A1 IMPROVING TOOL PERFORMANCE BY LINKING SPECTROSCOPIC INFORMATION WITH TOOL OPERATIONAL PARAMETERS AND MATERIAL MEASUREMENTS 审中-公开
标题翻译：通过连接光谱信息与工具操作参数和材料测量来提高工具性能
公开(公告)号：WO2012099907A1
公开(公告)日：2012-07-26
申请号：PCT/US2012/021614
申请日：2012-01-17
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , SUGISHIMA, Kenji
IPC分类号： H01L21/00
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。

5. 发明申请

WO2015085122A1 SYSTEM AND METHOD FOR LEARNING AND/OR OPTIMIZING MANUFACTURING PROCESSES 审中-公开
标题翻译：用于学习和/或优化制造过程的系统和方法
公开(公告)号：WO2015085122A1
公开(公告)日：2015-06-11
申请号：PCT/US2014/068672
申请日：2014-12-04
申请人： TOKYO ELECTRON LIMITED , PATEL, Sukesh Janubhai
发明人： PATEL, Sukesh Janubhai , KAUSHAL, Sanjeev
IPC分类号： G05B13/02
CPC分类号： G06N99/005 , G05B19/41885 , G05B2219/31464 , G05B2219/32017 , G05B2219/45032 , Y02P90/14 , Y02P90/22 , Y02P90/26 , Y02P90/86
摘要： A system and method for learning and/or optimizing processes related to semiconductor manufacturing is provided. A learning component generates a set of candidate process models based on process data associated with one or more fabrication tools. The learning component also selects a particular process model from the set of candidate process models that is associated with lowest error. An optimization component generates a set of candidate solutions associated with the particular process model. The optimization component also selects a particular solution from the set of candidate solutions based on a target output value and an output value associated with the particular solution.
摘要翻译：提供了一种用于学习和/或优化与半导体制造有关的过程的系统和方法。学习组件基于与一个或多个制造工具相关联的过程数据生成一组候选过程模型。学习组件还从与最低错误相关联的候选过程模型集中选择特定过程模型。优化组件生成与特定过程模型相关联的一组候选解。优化组件还基于与特定解决方案相关联的目标输出值和输出值从候选解决方案集中选择特定解决方案。

6. 发明申请

WO2014088670A1 METHOD AND APPARATUS FOR AUTONOMOUS IDENTIFICATION OF PARTICLE CONTAMINATION DUE TO ISOLATED PROCESS EVENTS AND SYSTEMATIC TRENDS 审中-公开
标题翻译：用于自动识别分离过程事件和系统趋势的颗粒污染的方法和装置
公开(公告)号：WO2014088670A1
公开(公告)日：2014-06-12
申请号：PCT/US2013/059722
申请日：2013-09-13
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev
发明人： KAUSHAL, Sanjeev , WATERMAN, Aaron Archer , MOROZUMI, Yuichiro , OZAKI, Tetsushi , PATEL, Sukesh Janubhai
IPC分类号： G05B19/418
CPC分类号： G05B19/41875 , G05B2219/31336 , G05B2219/32368 , G05B2219/37224 , G06F17/3053 , G06N99/005 , H01L21/67288
摘要： A system and method for autonomously tracing a cause of particle contamination during semiconductor manufacture is provided. A contamination analysis system analyzes tool process logs together with particle contamination data for multiple process runs to determine a relationship between systematic particle contamination levels and one or more tool parameters. This relationship is used to predict expected contamination levels associated with regular usage of the tool, and to identify which tool parameters have the largest impact on expected levels of particle contamination. The contamination analysis system also identifies process logs showing unexpected deviant particle contamination levels that exceed expected contamination levels, and traces the cause of the deviant particle contamination to particular process log parameter events.
摘要翻译：提供了一种用于在半导体制造期间自主追踪颗粒污染原因的系统和方法。污染分析系统分析工具过程日志以及多个过程运行的颗粒污染数据，以确定系统性颗粒污染水平与一个或多个工具参数之间的关系。这种关系用于预测与工具的常规使用相关的预期污染水平，并确定哪些工具参数对颗粒污染的预期水平具有最大的影响。污染分析系统还识别显示出超出预期污染水平的意外偏差颗粒污染水平的过程记录，并追踪特定过程日志参数事件的异常颗粒污染的原因。

7. 发明申请

WO2014074221A1 AUTONOMOUS TOOL PARAMETER IMPACT IDENTIFICATION FOR SEMICONDUCTOR MANUFACTURING 审中-公开
标题翻译：自主工具参数对半导体制造的影响识别
公开(公告)号：WO2014074221A1
公开(公告)日：2014-05-15
申请号：PCT/US2013/059715
申请日：2013-09-13
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai , POLAK, Wolfgang , WATERMAN, Aaron Archer , WOLFE, Orion
IPC分类号： H01L21/02 , G06F19/00
CPC分类号： G05B19/41875 , G05B2219/32187 , G05B2219/45031 , G06N99/005 , Y02P90/22 , Y02P90/86
摘要： A system and method for autonomously determining the impact of respective tool parameters on tool performance in a semiconductor manufacturing system is provided. A parameter impact identification system receives tool parameter and tool performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the actual behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.
摘要翻译：提供了一种用于自主确定各个工具参数对半导体制造系统中的工具性能的影响的系统和方法。参数影响识别系统接收用于半导体制造系统的一个或多个过程运行的工具参数和工具性能数据，并且针对每个工具参数生成表征工具性能指标的行为的单独函数，所述工具参数根据工具参数中的单个参数。然后根据功能预测工具性能指标的实际行为或者根据工具性能指标对单个工具参数的变化确定的灵敏度来评分每个功能。然后根据这些分数对工具参数进行排序，并根据排序推导出一组减少的关键工具参数。然后可以基于这组缩减的工具参数对工具性能指标进行建模。

8. 发明申请

WO2015175629A1 SYSTEM AND METHOD FOR MODELING AND/OR ANALYZING MANUFACTURING PROCESSES 审中-公开
标题翻译：用于建模和/或分析制造工艺的系统和方法
公开(公告)号：WO2015175629A1
公开(公告)日：2015-11-19
申请号：PCT/US2015/030519
申请日：2015-05-13
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON U.S. HOLDINGS, INC.
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh Janubhai
IPC分类号： G06F17/50 , G06F15/18
CPC分类号： G06N99/005 , G05B19/418 , G05B2219/45032 , G06F17/5009 , Y02P90/86
摘要： Systems and techniques for modeling and/or analyzing manufacturing processes are presented. A dataset component generates a plurality of binary classification datasets based on process data associated with one or more fabrication tools. A learning component generates a plurality of learned models based on the plurality of binary classification datasets and applies a weight to the plurality of learned models based on a number of data samples associated with the plurality of binary classification datasets to generate a weighted plurality of learned models. A merging component merges the weighted plurality of learned models to generate a process model for the process data.
摘要翻译：介绍了制造过程建模和/或分析的系统和技术。数据集组件基于与一个或多个制造工具相关联的过程数据生成多个二进制分类数据集。学习组件基于多个二进制分类数据集生成多个学习模型，并且基于与多个二进制分类数据集相关联的数据样本的数量向多个学习模型应用权重，以产生加权的多个学习模型。合并组件合并加权的多个学习模型以生成过程数据的过程模型。

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