公开(公告)号：US20070221125A1

公开(公告)日：2007-09-27

申请号：US11277467

申请日：2006-03-24

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Rao

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Rao

IPC分类号： G06F19/00 ,  B05C11/00 ,  H01L21/306

CPC分类号： H01L21/67253 ,  H01L21/67242 ,  H01L21/67248 ,  H01L21/67288 ,  H01L2924/0002 ,  H01L2924/00

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：US07141765B2

公开(公告)日：2006-11-28

申请号：US10473248

申请日：2002-03-20

申请人： Toshiyuki Makiya ,  Takanori Saito ,  Karuki Eickmann ,  Sanjeev Kaushal ,  Anthony Dip ,  David L. O'meara

发明人： Toshiyuki Makiya ,  Takanori Saito ,  Karuki Eickmann ,  Sanjeev Kaushal ,  Anthony Dip ,  David L. O'meara

IPC分类号： H05B1/02 ,  A21B1/00

CPC分类号： G01K7/04 ,  G01K1/20

摘要： A antireflective film 50 is formed on a thermocouple 42 arranged in a processing vessel 1 of a heat treatment apparatus in order to improve the transient response characteristics of the thermocouple 42. In a typical embodiment, the thermocouple 42 is made by connecting a platinum wire 43A and a platinum-rhodium alloy wire 43B, and the antireflective film 50 is composed by stacking a silicon nitride layer 50C, silicon layer 50B and a silicon nitride layer 50A in that order.

摘要翻译： 为了改善热电偶42的瞬态响应特性，在设置在热处理装置的处理容器1中的热电偶42上形成防反射膜50。 在一个典型的实施例中，热电偶42是通过连接铂线43A和铂 - 铑合金线43B制成的，并且抗反射膜50由叠氮化硅层50C，硅层50B和硅 氮化物层50A。

公开(公告)号：US08725667B2

公开(公告)日：2014-05-13

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： G06F15/18 ,  G06N3/00 ,  G06F17/00 ,  G06N5/02 ,  G06N7/00 ,  G06N7/08 ,  G06F19/00

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：US20120242667A1

公开(公告)日：2012-09-27

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：US20120209798A1

公开(公告)日：2012-08-16

申请号：US13457830

申请日：2012-04-27

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

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

IPC分类号： G06N3/12

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。

公开(公告)号：US07710565B2

公开(公告)日：2010-05-04

申请号：US11956777

申请日：2007-12-14

申请人： Sanjeev Kaushal ,  Sairam Sankaranarayanan ,  Kenji Sugishima

发明人： Sanjeev Kaushal ,  Sairam Sankaranarayanan ,  Kenji Sugishima

IPC分类号： G01B9/08

CPC分类号： G01B21/045

摘要： A method for correcting systematic errors in an optical measurement tool in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.

摘要翻译： 一种用于校正光学测量工具中的系统误差的方法，其中通过将标准衬底暴露于电磁能谱，从包括具有已知折射率和已知消光系数的层的标准衬底测量第一衍射光谱。 计算标准底物的工具完美衍射光谱。 通过将测量的衍射光谱与计算的工具完美衍射光谱进行比较来计算硬件系统误差。 通过将工件暴露于电磁能的频谱来测量来自工件的第二衍射光谱，并且基于所计算的硬件系统误差来校正所测量的第二衍射光谱，以获得校正的衍射光谱。

公开(公告)号：US20090157343A1

公开(公告)日：2009-06-18

申请号：US11956777

申请日：2007-12-14

申请人： Sanjeev Kaushal ,  Sairam Sankaranarayanan ,  Kenji Sugishima

发明人： Sanjeev Kaushal ,  Sairam Sankaranarayanan ,  Kenji Sugishima

IPC分类号： G01B11/02

CPC分类号： G01B21/045

摘要： A method for correcting systematic errors in an optical measurement tool in which a first diffraction spectrum is measured from a standard substrate including a layer having a known refractive index and a known extinction coefficient by exposing the standard substrate to a spectrum of electromagnetic energy. A tool-perfect diffraction spectrum is calculated for the standard substrate. A hardware systematic error is calculated by comparing the measured diffraction spectrum to the calculated tool-perfect diffraction spectrum. A second diffraction spectrum from a workpiece is measured by exposing the workpiece to the spectrum of electromagnetic energy, and the measured second diffraction spectrum is corrected based on the calculated hardware systematic error to obtain a corrected diffraction spectrum.

摘要翻译： 一种用于校正光学测量工具中的系统误差的方法，其中通过将标准衬底暴露于电磁能谱，从包括具有已知折射率和已知消光系数的层的标准衬底测量第一衍射光谱。 计算标准底物的工具完美衍射光谱。 通过将测量的衍射光谱与计算的工具完美衍射光谱进行比较来计算硬件系统误差。 通过将工件暴露于电磁能的频谱来测量来自工件的第二衍射光谱，并且基于所计算的硬件系统误差来校正所测量的第二衍射光谱，以获得校正的衍射光谱。

公开(公告)号：US20080017843A1

公开(公告)日：2008-01-24

申请号：US11488752

申请日：2006-07-19

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Swaroop Ganguly

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Swaroop Ganguly

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：包含硅的衬底; 形成在所述基板上的沟道区域; 旋转注入器，其形成在所述沟道区域的第一侧的所述衬底上并且被配置为将自旋极化电流扩散到所述沟道区域中; 旋转检测器，形成在所述沟道区域的第二侧的所述衬底上，并被配置为从所述沟道区域接收所述自旋极化电流; 以及在所述沟道区域的区域中形成在所述衬底上的栅极。

公开(公告)号：US20070224712A1

公开(公告)日：2007-09-27

申请号：US11277448

申请日：2006-03-24

申请人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Rao

发明人： Sanjeev Kaushal ,  Kenji Sugishima ,  Donthineni Rao

IPC分类号： H01L21/66 ,  H01L21/00 ,  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：US20150332167A1

公开(公告)日：2015-11-19

申请号：US14276349

申请日：2014-05-13

申请人： Sanjeev Kaushal ,  Sukesh Janubhai Patel

发明人： Sanjeev Kaushal ,  Sukesh Janubhai Patel

IPC分类号： G06N99/00 ,  G05B19/418

CPC分类号： G06N20/00 ,  G05B19/418 ,  G05B2219/45032 ,  G06F17/5009 ,  Y02P90/14 ,  Y02P90/26 ,  Y02P90/86

摘要： Systems and techniques for modeling and/or analyzing manufacturing processes are presented. A dataset component generates a plurality of binary classification datasets based on process data associated with one or more fabrication tools. A learning component generates a plurality of learned models based on the plurality of binary classification datasets and applies a weight to the plurality of learned models based on a number of data samples associated with the plurality of binary classification datasets to generate a weighted plurality of learned models. A merging component merges the weighted plurality of learned models to generate a process model for the process data.

摘要翻译： 介绍了制造过程建模和/或分析的系统和技术。 数据集组件基于与一个或多个制造工具相关联的过程数据生成多个二进制分类数据集。 学习组件基于多个二进制分类数据集生成多个学习模型，并且基于与多个二进制分类数据集相关联的数据样本的数量向多个学习模型应用权重，以产生加权的多个学习模型 。 合并组件合并加权的多个学习模型以生成过程数据的过程模型。