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11.发明授权公开(公告)号：US09396443B2
公开(公告)日：2016-07-19
申请号：US14097907
申请日：2013-12-05
IPC分类号： G06F17/00 , G06N5/02 , G06N99/00 , G05B19/418
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。
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公开(公告)号：US20140304196A1
公开(公告)日：2014-10-09
申请号：US14247191
申请日：2014-04-07
发明人： Sanjeev Kaushal , Kenji Sugishima , Sukesh Janubhai Patel , Robert Filman , Wolfgang Polak , Orion Wolfe , Jessie Burger
IPC分类号： G06N99/00 , G06F3/0484
CPC分类号： G06N20/00 , 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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13.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：US20130151447A1
公开(公告)日：2013-06-13
申请号：US13763797
申请日：2013-02-11
IPC分类号： G06N99/00
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。
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14.发明申请公开(公告)号：US20170329318A1
公开(公告)日：2017-11-16
申请号：US15666765
申请日：2017-08-02
IPC分类号： G05B19/418
CPC分类号： G05B19/41875 , G05B2219/32187 , G05B2219/45031 , G06N20/00 , 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 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.
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公开(公告)号：US20160307116A1
公开(公告)日：2016-10-20
申请号：US15195380
申请日：2016-06-28
IPC分类号： G06N99/00 , G05B19/418
CPC分类号： G06N20/00 , 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.
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16.发明申请公开(公告)号：US20150161520A1
公开(公告)日：2015-06-11
申请号：US14097907
申请日：2013-12-05
IPC分类号： G06N99/00 , G06N3/12 , G05B19/418
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。
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17.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：US20140229409A1
公开(公告)日：2014-08-14
申请号：US14259696
申请日：2014-04-23
IPC分类号： G06N99/00
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。
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18.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：US08744607B2
公开(公告)日：2014-06-03
申请号：US13763797
申请日：2013-02-11
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。
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