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1. 发明申请

WO2010114641A1 METHOD AND SYSTEM FOR DETECTION OF TOOL PERFORMANCE DEGRADATION AND MISMATCH 审中-公开
标题翻译：用于检测工具性能降低和误差的方法和系统
公开(公告)号：WO2010114641A1
公开(公告)日：2010-10-07
申请号：PCT/US2010/023113
申请日：2010-02-03
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , PATEL, Sukesh, J. , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PATEL, Sukesh, J. , SUGISHIMA, Kenji
IPC分类号： G06F15/18
CPC分类号： G05B13/0265 , G05B19/4065 , G05B2219/33041 , G05B2219/37252 , G06N5/04 , G06N99/005 , Y02P90/86
摘要： Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch 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. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.
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性能下降和失配的方法。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。可以利用从合成或生产数据中获取的客观生成的知识来确定特定输出变量与一组相关影响变量之间的数学关系。生成的关系有助于评估一组工具的性能下降，以及其中的工具之间的性能不匹配。

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

WO2008111991A2 SPINTRONIC TRANSISTOR 审中-公开
标题翻译：自旋晶体管
公开(公告)号：WO2008111991A2
公开(公告)日：2008-09-18
申请号：PCT/US2007/073099
申请日：2007-07-10
申请人： TOKYO ELECTRON LIMITED , KAUSHAL, Sanjeev , SUGISHIMA, Kenji , GANGULY, Swaroop
发明人： KAUSHAL, Sanjeev , SUGISHIMA, Kenji , GANGULY, Swaroop
IPC分类号： H01L31/00
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 said spin polarized current from the channel region; and a gate formed on the substrate in an area of said channel region,
摘要翻译：一种半导体器件，包括：包括硅的衬底：形成在衬底上的沟道区; 自旋注入器，在所述沟道区的第一侧处形成在所述衬底上，并被配置为将自旋极化电流扩散到所述沟道区中; 自旋检测器，所述自旋检测器在所述沟道区的第二侧处形成在所述衬底上并且被配置为从所述沟道区接收所述自旋极化电流; 以及在所述沟道区域的区域中的衬底上形成的栅极，

4. 发明申请

WO2007115136A1 MONITORING A MONOLAYER DEPOSITION (MLD) SYSTEM USING A BUILT-IN SELF TEST (BIST) TABLE 审中-公开
标题翻译：使用内置自检（BIST）表监测单层沉积（MLD）系统
公开(公告)号：WO2007115136A1
公开(公告)日：2007-10-11
申请号：PCT/US2007/065568
申请日：2007-03-30
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC , KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
IPC分类号： G05B19/418 , G05B23/02
CPC分类号： G05B19/41875 , C23C16/45546 , C23C16/52 , G05B23/0254 , G05B2219/11 , G05B2219/1112 , G05B2219/37514 , G05B2219/37533 , G05B2219/45031 , Y02P90/14 , Y02P90/22 , Y02P90/26
摘要： A method of monitoring a processing system (100, 200, 300) in real-time using low-pressure based modeling techniques that include processing one or more of wafers (W) in a processing chamber (50, 104, 202); 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：一种监视处理系统（100,200,300）的方法，用于实时使用基于低压的建模技术，其包括处理处理室（50,104,202）中的一个或多个晶片（W）; 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。

5. 发明申请

WO2006081023A2 METHOD AND APPARATUS FOR MONOLAYER DEPOSITION (MLD) 审中-公开
标题翻译：单层沉积方法和装置（MLD）
公开(公告)号：WO2006081023A2
公开(公告)日：2006-08-03
申请号：PCT/US2005/046495
申请日：2005-12-21
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC. , KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
IPC分类号： C23C16/455 , C30B25/14 , C30B15/16 , C23C16/52
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。

6. 发明申请

WO2007115084A1 MONITORING A SYSTEM DURING LOW-PRESSURE PROCESSES 审中-公开
标题翻译：在低压过程中监测系统
公开(公告)号：WO2007115084A1
公开(公告)日：2007-10-11
申请号：PCT/US2007/065483
申请日：2007-03-29
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC. , KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
IPC分类号： G05B19/418 , G05B23/02
CPC分类号： G05B19/41875 , C23C16/52 , G05B23/0254 , G05B2219/37514 , G05B2219/37533 , G05B2219/45031 , Y02P90/22 , Y02P90/26
摘要： A method of monitoring a processing system (100, 200, 300) in real-time using low-pressure based modeling techniques that include processing one or more of wafers (W) in a processing chamber (50, 104, 202); 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：一种监视处理系统（100,200,300）的方法，用于实时使用基于低压的建模技术，其包括处理处理室（50,104,202）中的一个或多个晶片（W）; 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。

7. 发明申请

WO2007030194A1 BUILT-IN SELF TEST FOR A THERMAL PROCESSING SYSTEM 审中-公开
标题翻译：用于热处理系统的内置自检
公开(公告)号：WO2007030194A1
公开(公告)日：2007-03-15
申请号：PCT/US2006/026220
申请日：2006-07-06
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC. , KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji , DIP, Anthony , SMITH, David , JOE, Raymond , GANDHI, Sundar
发明人： KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji , DIP, Anthony , SMITH, David , JOE, Raymond , GANDHI, Sundar
CPC分类号： H01L22/10 , G05B23/0254 , H01L21/67248
摘要： A method of monitoring a thermal processing system (100, 200) in real-time using a built-in self test (BIST) table that includes positioning a plurality of wafers (W) in a processing chamber (202) in the thermal processing system (100, 200); executing a real-time dynamic model (330) to generate a predicted dynamic process response for the processing chamber (202) during the processing time; creating a first measured dynamic process response; determining a dynamic estimation error using a difference between the predicted dynamic process response and the measured dynamic process response; and comparing the dynamic estimation error to operational thresholds established by one or more rules in the BiST table.
摘要翻译：一种使用内置自检（BIST）表实时监测热处理系统（100,200）的方法，其包括将多个晶片（W）定位在热处理系统中的处理室（202）中（100,200）; 执行实时动态模型（330）以在处理时间期间生成用于处理室（202）的预测动态过程响应; 创建第一个测量动态过程响应; 使用预测的动态过程响应和测量的动态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由BiST表中的一个或多个规则建立的操作阈值进行比较。

8. 发明申请

WO2007112181A3 METHOD OF MONITORING A SEMICONDUCTOR PROCESSING SYSTEM USING A WIRELESS SENSOR NETWORK 审中-公开
公开(公告)号：WO2007112181A3
公开(公告)日：2007-10-04
申请号：PCT/US2007/063466
申请日：2007-03-07
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC. , KAUSHAL, Sanjeev , SUGISHIMA, Kenji , RAO, Donthineni, Ramesh Kumar
发明人： KAUSHAL, Sanjeev , SUGISHIMA, Kenji , RAO, Donthineni, Ramesh Kumar
IPC分类号： H01L21/00
摘要： A method and system for non-invasive sensing and monitoring of a processing system (100, 200) employed in semiconductor manufacturing. The method allows for detecting and diagnosing drift and failures in the processing system (100, 200) and taking the appropriate correcting measures. The method includes positioning at least one non-invasive sensor (247a-d, 248a-b, 249a-b, 250a-c, 906) on an outer surface of a system component of the processing system (200), where the at least one non- invasive sensor forms a wireless sensor network (902), acquiring a sensor signal from the at least one non-invasive sensor (247a-d, 248a-b, 249a-b, 250a-c, 906), 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 (902) to store and process the sensor signal. In one embodiment, the non-invasive sensor (247a-d, 248a-b, 249a-b, 250a-c, 906) can be an accelerometer sensor and the wireless sensor network (902) can be motes-based.

9. 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。

10. 发明申请

WO2007115138A1 METHOD FOR CREATING A BUILT-IN SELF TEST (BIST) TABLE FOR MONITORING A MONOLAYER DEPOSITION (MLD) SYSTEM 审中-公开
标题翻译：一种用于监测单层沉积（MLD）系统的内置自检（BIST）表的方法
公开(公告)号：WO2007115138A1
公开(公告)日：2007-10-11
申请号：PCT/US2007/065573
申请日：2007-03-30
申请人： TOKYO ELECTRON LIMITED , TOKYO ELECTRON AMERICA, INC. , KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
发明人： KAUSHAL, Sanjeev , PANDEY, Pradeep , SUGISHIMA, Kenji
IPC分类号： G05B19/418 , G05B23/02
CPC分类号： G05B19/41875 , C23C16/45546 , C23C16/52 , G05B23/0254 , G05B2219/37514 , G05B2219/37533 , G05B2219/45031 , Y02P90/22 , Y02P90/26
摘要： A method of monitoring a processing system (100, 200, 300) in real-time using low-pressure based modeling techniques that include processing one or more of wafers (W) in a processing chamber (50, 104, 202), calculating dynamic estimation errors for the precursor and/or purging process, and determining if the dynamic estimation errors can be associated with pre-existing BIST rules for the process. When the dynamic estimation error cannot be associated with a pre-existing BIST rule, the method includes either modifying the BlST table by creating a new BIST rule for the process, or stopping the process when a new BIST rule cannot be created.
摘要翻译：一种使用基于低压的建模技术来实时监控处理系统（100,200,300）的方法，所述建模技术包括处理处理室（50,104,202）中的一个或多个晶片（W），计算动态前导和/或清除过程的估计误差，以及确定动态估计误差是否可以与该过程的预先存在的BIST规则相关联。当动态估计错误不能与预先存在的BIST规则相关联时，该方法包括通过为进程创建一个新的BIST规则来修改BlST表，或者在无法创建新的BIST规则时停止该进程。

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