公开(公告)号：US20060166501A1

公开(公告)日：2006-07-27

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

IPC分类号： H01L21/44

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。

公开(公告)号：US20050167514A1

公开(公告)日：2005-08-04

申请号：US10769623

申请日：2004-01-30

申请人： Sanjeev Kaushal ,  Pradeep Pandey ,  Kenji Sugishima

发明人： Sanjeev Kaushal ,  Pradeep Pandey ,  Kenji Sugishima

IPC分类号： F23N1/00 ,  G03F20060101 ,  G03F1/14 ,  G03F7/20

CPC分类号： G03F1/68 ,  G03F1/78 ,  H01L21/67248

摘要： An adaptive real time thermal processing system is presented that includes a multivariable controller. Generally, the method includes creating a dynamic model of the thermal processing system; incorporating reticle/mask curvature in the dynamic model; coupling a diffusion-amplification model into the dynamic thermal model; creating a multivariable controller; parameterizing the nominal setpoints into a vector of intelligent setpoints; creating a process sensitivity matrix; creating intelligent setpoints using an efficient optimization method and process data; and establishing recipes that select appropriate models and setpoints during run-time.

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。 通常，该方法包括创建热处理系统的动态模型; 在动态模型中结合掩模/掩模曲率; 将扩散扩增模型耦合到动态热模型中; 创建一个多变量控制器; 将标称设定值参数化为智能设定点的向量; 创建一个过程敏感性矩阵; 使用有效的优化方法和过程数据创建智能设定点; 并建立在运行期间选择合适的模型和设定值的配方。

公开(公告)号：US20050149886A1

公开(公告)日：2005-07-07

申请号：US10747842

申请日：2003-12-29

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Pradeep Pandey

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Pradeep Pandey

IPC分类号： F25B21/02 ,  H01L21/00 ,  G06F17/50

CPC分类号： H01L21/67248

摘要： Methods for adaptive real time control of a system for thermal processing substrates, such as semiconductor wafers and display panels. Generally, the method includes creating a dynamic model of the thermal processing system, incorporating wafer bow in the dynamic model, coupling a diffusion-amplification model into the dynamic thermal model, creating a multivariable controller, parameterizing the nominal setpoints, creating a process sensitivity matrix, creating intelligent setpoints using an efficient optimization method and process data, and establishing recipes that select appropriate models and setpoints during run-time.

摘要翻译： 用于热处理衬底（例如半导体晶片和显示面板）的系统的自适应实时控制的方法。 通常，该方法包括创建热处理系统的动态模型，将动态模型中的晶圆弓结合在一起，将扩散放大模型耦合到动态热模型中，创建多变量控制器，参数化标称设定点，创建过程灵敏度矩阵 ，使用有效的优化方法和过程数据创建智能设定点，以及建立在运行时选择合适的模型和设定值的配方。

公开(公告)号：US07737051B2

公开(公告)日：2010-06-15

申请号：US10797876

申请日：2004-03-10

申请人： Anthony Dip ,  Pradip K. Roy ,  Sanjeev Kaushal ,  Allen J. Leith ,  Seungho Oh ,  Raymond Joe

发明人： Anthony Dip ,  Pradip K. Roy ,  Sanjeev Kaushal ,  Allen J. Leith ,  Seungho Oh ,  Raymond Joe

IPC分类号： H01L21/36

CPC分类号： H01L21/31604 ,  H01L21/02142 ,  H01L21/0217 ,  H01L21/02175 ,  H01L21/02318 ,  H01L21/02323 ,  H01L21/02337 ,  H01L21/02362 ,  H01L21/31637 ,  H01L21/31641 ,  H01L21/31645 ,  H01L21/3185 ,  Y10T29/41

摘要： A method for using a silicon germanium (SiGe) surface layer to integrate a high-k dielectric layer into a semiconductor device. The method forms a SiGe surface layer on a substrate and deposits a high-k dielectric layer on the SiGe surface layer. An oxide layer, located between the high-k dielectric layer and an unreacted portion of the SiGe surface layer, is formed during one or both of deposition of the high-k dielectric layer and an annealing process after deposition of the high-k dielectric layer. The method further includes forming an electrode layer on the high-k dielectric layer.

摘要翻译： 一种使用硅锗（SiGe）表面层将高k电介质层集成到半导体器件中的方法。 该方法在衬底上形成SiGe表面层，并在SiGe表面层上沉积高k电介质层。 在高k电介质层的沉积和沉积高k电介质层之间的退火工艺中的一个或两个期间，形成位于高k电介质层和SiGe表面层的未反应部分之间的氧化物层 。 该方法还包括在高k电介质层上形成电极层。

公开(公告)号：US20050199877A1

公开(公告)日：2005-09-15

申请号：US10797876

申请日：2004-03-10

申请人： Anthony Dip ,  Pradip Roy ,  Sanjeev Kaushal ,  Allen Leith ,  Seungho Oh ,  Raymond Joe

发明人： Anthony Dip ,  Pradip Roy ,  Sanjeev Kaushal ,  Allen Leith ,  Seungho Oh ,  Raymond Joe

IPC分类号： H01L21/28 ,  H01L21/316 ,  H01L21/318 ,  H01L29/51 ,  H01L29/12 ,  H01L21/36 ,  H01L21/3205

CPC分类号： H01L21/31604 ,  H01L21/02142 ,  H01L21/0217 ,  H01L21/02175 ,  H01L21/02318 ,  H01L21/02323 ,  H01L21/02337 ,  H01L21/02362 ,  H01L21/31637 ,  H01L21/31641 ,  H01L21/31645 ,  H01L21/3185 ,  Y10T29/41

摘要： A method for using a silicon germanium (SiGe) surface layer to integrate a high-k dielectric layer into a semiconductor device. The method forms a SiGe surface layer on a substrate and deposits a high-k dielectric layer on the SiGe surface layer. An oxide layer, located between the high-k dielectric layer and an unreacted portion of the SiGe surface layer, is formed during one or both of deposition of the high-k dielectric layer and an annealing process after deposition of the high-k dielectric layer. The method further includes forming an electrode layer on the high-k dielectric layer.

摘要翻译： 一种使用硅锗（SiGe）表面层将高k电介质层集成到半导体器件中的方法。 该方法在衬底上形成SiGe表面层，并在SiGe表面层上沉积高k电介质层。 在高k电介质层的沉积和沉积高k电介质层之间的退火工艺中的一个或两个期间，形成位于高k电介质层和SiGe表面层的未反应部分之间的氧化物层 。 该方法还包括在高k电介质层上形成电极层。

公开(公告)号：US20090240366A1

公开(公告)日：2009-09-24

申请号：US12416018

申请日：2009-03-31

申请人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Kenji Sugishima

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

IPC分类号： G06F17/00 ,  G06F15/18 ,  G06N3/12 ,  G06N7/00

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，并且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。

公开(公告)号：US08723869B2

公开(公告)日：2014-05-13

申请号：US13052943

申请日：2011-03-21

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Sukesh Janubhai Patel ,  Robert Filman ,  Wolfgang Polak ,  Orion Wolfe ,  Jessie Burger

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Sukesh Janubhai Patel ,  Robert Filman ,  Wolfgang Polak ,  Orion Wolfe ,  Jessie Burger

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。

公开(公告)号：US20120185813A1

公开(公告)日：2012-07-19

申请号：US13009685

申请日：2011-01-19

申请人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Kenji Sugishima

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

IPC分类号： G06F17/50

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。

公开(公告)号：US08026113B2

公开(公告)日：2011-09-27

申请号：US11277448

申请日：2006-03-24

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Ramesh Kumar Rao

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Ramesh Kumar Rao

IPC分类号： H01L21/66 ,  G01R31/26

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。

公开(公告)号：US20090228408A1

公开(公告)日：2009-09-10

申请号：US12044959

申请日：2008-03-08

申请人： Sanjeev Kaushal ,  Sukesh Janubhai Patel ,  Kenji Sugishima

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

IPC分类号： G06F15/18 ,  G06F11/07

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。