公开(公告)号：US10133265B2

公开(公告)日：2018-11-20

申请号：US15219467

申请日：2016-07-26

申请人： Tokyo Electron Limited

发明人： Aaron Archer Waterman ,  Yuichiro Morozumi ,  Tetsushi Ozaki ,  Sanjeev Kaushal ,  Sukesh Janubhai Patel

IPC分类号： G05B19/41 ,  G05B19/418 ,  H01L21/67 ,  G06N99/00 ,  G06F17/30

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.

公开(公告)号：US20170023927A1

公开(公告)日：2017-01-26

申请号：US14805793

申请日：2015-07-22

申请人： Tokyo Electron Limited

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Wolfgang Polak ,  Orion Wolfe

IPC分类号： G05B19/4065

CPC分类号： G05B19/4065 ,  G05B2219/37242 ,  G05B2219/37245 ,  G05B2219/45031

摘要： Systems and techniques to facilitate tool failure analysis associated with fabrication processes are presented. A monitoring component determines a candidate tool failure associated with one or more fabrication tools based on sensor data generated by a set of sensors associated with the one or more fabrication tools. A signature component generates a signature dataset for the candidate tool failure based on data associated with the one or more fabrication tools. A comparison component compares the candidate tool failure to at least one previously determined tool failure based on the signature dataset and at least one other signature dataset associated with the at least one previously determined tool failure.

摘要翻译： 介绍了有助于与制造工艺相关的工具故障分析的系统和技术。 监测部件基于由与一个或多个制造工具相关联的一组传感器生成的传感器数据来确定与一个或多个制造工具相关联的候选工具故障。 签名组件基于与一个或多个制造工具相关联的数据来生成用于候选工具故障的签名数据集。 比较部件将候选工具故障与基于签名数据集的至少一个先前确定的工具故障和与至少一个先前确定的工具故障相关联的至少一个其他签名数据集进行比较。

公开(公告)号：US20140163712A1

公开(公告)日：2014-06-12

申请号：US13706712

申请日：2012-12-06

申请人： TOKYO ELECTRON LIMITED

发明人： Aaron Archer Waterman ,  Yuichiro Morozumi ,  Tetsushi Ozaki ,  Sanjeev Kaushal ,  Sukesh Janubhai Patel

IPC分类号： G06F17/40

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。

公开(公告)号：US20140135970A1

公开(公告)日：2014-05-15

申请号：US13673306

申请日：2012-11-09

申请人： TOKYO ELECTRON LIMITED

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Wolfgang Polak ,  Aaron Archer Waterman ,  Orion Wolfe

IPC分类号： G05B15/00

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： 提供了一种用于在半导体制造系统中自主确定各个刀具参数对刀具性能的影响的系统和方法。 参数冲击识别系统接收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，并且根据单个工具参数为每个刀具参数生成表征刀具性能指标行为的单独功能 。 然后根据功能预测刀具性能指标的实际行为，或者根据刀具性能指标对单刀具参数变化的确定灵敏度对每个功能进行刻痕。 然后根据这些分数对工具参数进行排名，并根据排名得出一组关键工具参数。 然后，可以基于这组减少的刀具参数来建模刀具性能指标。

公开(公告)号：US20160334782A1

公开(公告)日：2016-11-17

申请号：US15219467

申请日：2016-07-26

申请人： Tokyo Electron Limited

发明人： Aaron Archer Waterman ,  Yuichiro Morozumi ,  Tetsushi Ozaki ,  Sanjeev Kaushal ,  Sukesh Janubhai Patel

IPC分类号： G05B19/418 ,  G06F17/30 ,  G06N99/00 ,  H01L21/67

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。

公开(公告)号：US09424528B2

公开(公告)日：2016-08-23

申请号：US14259696

申请日：2014-04-23

申请人： Tokyo Electron Limited

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Kenji Sugishima

IPC分类号： G06N99/00 ,  G05B13/02 ,  G06N5/04

CPC分类号： G06N99/005 ,  G05B13/0265 ,  G06N5/04

摘要： Performance of a manufacturing tool is optimized. 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： 优化了制造工具的性能。 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。

公开(公告)号：US09405289B2

公开(公告)日：2016-08-02

申请号：US13706712

申请日：2012-12-06

申请人： Tokyo Electron Limited

发明人： Aaron Archer Waterman ,  Yuichiro Morozumi ,  Tetsushi Ozaki ,  Sanjeev Kaushal ,  Sukesh Janubhai Patel

IPC分类号： G06F17/40 ,  G05B19/418 ,  H01L21/67 ,  G06N99/00

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。

公开(公告)号：US10571900B2

公开(公告)日：2020-02-25

申请号：US15666765

申请日：2017-08-02

申请人： Tokyo Electron Limited

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Wolfgang Polak ,  Aaron Archer Waterman ,  Orion Wolfe

IPC分类号： G05B19/418 ,  G06N20/00

摘要： A system and method autonomously determines the impact of respective tool parameters on tool performance in a semiconductor manufacturing system. 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.

公开(公告)号：US10228678B2

公开(公告)日：2019-03-12

申请号：US14805793

申请日：2015-07-22

申请人： Tokyo Electron Limited

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Wolfgang Polak ,  Orion Wolfe

IPC分类号： G06F19/00 ,  G05B19/4065

摘要： Systems and techniques to facilitate tool failure analysis associated with fabrication processes are presented. A monitoring component determines a candidate tool failure associated with one or more fabrication tools based on sensor data generated by a set of sensors associated with the one or more fabrication tools. A signature component generates a signature dataset for the candidate tool failure based on data associated with the one or more fabrication tools. A comparison component compares the candidate tool failure to at least one previously determined tool failure based on the signature dataset and at least one other signature dataset associated with the at least one previously determined tool failure.

公开(公告)号：US09746849B2

公开(公告)日：2017-08-29

申请号：US13673306

申请日：2012-11-09

申请人： Tokyo Electron Limited

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Wolfgang Polak ,  Aaron Archer Waterman ,  Orion Wolfe

IPC分类号： G05B19/418 ,  G06N99/00

CPC分类号： G05B19/41875 ,  G05B2219/32187 ,  G05B2219/45031 ,  G06N99/005 ,  Y02P90/22 ,  Y02P90/86

摘要： A system and method autonomously determines the impact of respective tool parameters on tool performance in a semiconductor manufacturing system. A parameter impact identification system receives tool parameter and 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 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.