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11.发明授权Method of monitoring a semiconductor processing system using a wireless sensor network 有权使用无线传感器网络监测半导体处理系统的方法公开(公告)号：US08026113B2
公开(公告)日：2011-09-27
申请号：US11277448
申请日：2006-03-24
CPC分类号： H01L21/67253
摘要： A method and system for non-invasive sensing and monitoring of a processing system employed in semiconductor manufacturing. The method allows for detecting and diagnosing drift and failures in the processing system and taking the appropriate correcting measures. The method includes positioning at least one non-invasive sensor on an outer surface of a system component of the processing system, where the at least one invasive sensor forms a wireless sensor network, acquiring a sensor signal from the at least one non-invasive sensor, where the sensor signal tracks a gradual or abrupt change in a processing state of the system component during flow of a process gas in contact with the system component, and extracting the sensor signal from the wireless sensor network to store and process the sensor signal. In one embodiment, the non-invasive sensor can be an accelerometer sensor and the wireless sensor network can be motes-based.
摘要翻译： 用于半导体制造中采用的处理系统的非侵入式感测和监测的方法和系统。 该方法允许检测和诊断处理系统中的漂移和故障，并采取适当的校正措施。 该方法包括将至少一个非侵入式传感器定位在处理系统的系统部件的外表面上，其中至少一个侵入式传感器形成无线传感器网络，从至少一个非侵入式传感器获取传感器信号 ，其中传感器信号跟踪与系统部件接触的处理气体的流动期间系统部件的处理状态的逐渐或突然变化，以及从无线传感器网络提取传感器信号以存储和处理传感器信号。 在一个实施例中，非侵入式传感器可以是加速度计传感器，并且无线传感器网络可以是基于动机的。
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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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13.发明授权Monitoring a monolayer deposition (MLD) system using a built-in self test (BIST) table 失效使用内置自检（BIST）表监测单层沉积（MLD）系统公开(公告)号：US07519885B2
公开(公告)日：2009-04-14
申请号：US11278382
申请日：2006-03-31
申请人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
发明人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
CPC分类号： G05B19/41875 , C23C16/45546 , C23C16/52 , G05B23/0254 , G05B2219/1112 , G05B2219/37514 , G05B2219/37533 , G05B2219/45031 , G05B2223/02 , Y02P90/14 , Y02P90/22 , Y02P90/26
摘要： A method of monitoring a processing system in real-time using low-pressure based modeling techniques that include processing one or more of wafers in a processing chamber; determining a measured dynamic process response for a rate of change for a process parameter; executing a real-time dynamic model to generate a predicted dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the expected process response; and comparing the dynamic estimation error to operational limits.
摘要翻译： 一种使用基于低压的建模技术实时监控处理系统的方法，所述技术包括处理处理室中的一个或多个晶片; 确定针对过程参数的变化率的测量的动态过程响应; 执行实时动态模型以产生预测的动态过程响应; 使用预测的动态过程响应和预期过程响应之间的差来确定动态估计误差; 并将动态估计误差与运算极限进行比较。
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公开(公告)号：US07459175B2
公开(公告)日：2008-12-02
申请号：US11043459
申请日：2005-01-26
申请人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
发明人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
CPC分类号： C23C16/45527 , C23C16/45546 , C23C16/45557 , C23C16/46 , C23C16/52
摘要： An adaptive real time thermal processing system is presented that includes a multivariable controller. The method includes creating a dynamic model of the MLD processing system and incorporating virtual sensors in the dynamic model. The method includes using process recipes comprising intelligent set points, dynamic models, and/or virtual sensors.
摘要翻译： 提出了一种包括多变量控制器的自适应实时热处理系统。 该方法包括创建MLD处理系统的动态模型，并将虚拟传感器并入动态模型中。 该方法包括使用包括智能设定点，动态模型和/或虚拟传感器的过程配方。
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公开(公告)号：US07406644B2
公开(公告)日：2008-07-29
申请号：US11278012
申请日：2006-03-30
申请人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
发明人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
IPC分类号： G01R31/28
CPC分类号： H01L22/20
摘要： A method of monitoring a thermal processing system in real-time using a built-in self test (BIST) table to detect, diagnose and/or predict fault conditions and/or degraded performance. The method includes positioning a plurality of wafers in a processing chamber in the thermal processing system, performing a self test process, determining a real-time transient error from a measured transient response and a baseline transient response determined by a BIST rule stored in the BIST table, and comparing the transient error to operational limits and warning limits established by the BIST rule for the self test process.
摘要翻译： 使用内置自检（BIST）表来实时监测热处理系统的方法来检测，诊断和/或预测故障状况和/或降级的性能。 该方法包括将多个晶片定位在热处理系统中的处理室中，执行自测试过程，从测量的瞬态响应确定实时瞬态误差，以及由存储在BIST中的BIST规则确定的基线瞬态响应 表，并将瞬态误差与BIST规则为自检过程建立的运行极限和警告限制进行比较。
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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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17.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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19.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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公开(公告)号：US07838072B2
公开(公告)日：2010-11-23
申请号：US11043199
申请日：2005-01-26
申请人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
发明人： Sanjeev Kaushal , Pradeep Pandey , Kenji Sugishima
IPC分类号： C23C16/00
CPC分类号： C23C16/45527 , C23C16/52
摘要： An adaptive real time thermal processing system is presented that includes a multivariable controller. The method includes creating a dynamic model of the MLD processing system and incorporating virtual sensors in the dynamic model. The method includes using process recipes comprising intelligent set points, dynamic models, and/or virtual sensors.
摘要翻译： 提出了一种包括多变量控制器的自适应实时热处理系统。 该方法包括创建MLD处理系统的动态模型，并将虚拟传感器并入动态模型中。 该方法包括使用包括智能设定点，动态模型和/或虚拟传感器的过程配方。
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