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    Method and system for wafer quality predictive modeling based on multi-source information with heterogeneous relatedness
    1.
    发明授权
    Method and system for wafer quality predictive modeling based on multi-source information with heterogeneous relatedness 有权
    基于具有异构相关性的多源信息的晶圆质量预测建模方法和系统

    公开(公告)号：US09176183B2

    公开(公告)日：2015-11-03

    申请号：US13651974

    申请日：2012-10-15

    申请人： International Business Machines Corporation

    发明人： Yada Zhu ,  Jingrui He ,  Robert Jeffrey Baseman

    IPC分类号： G06F19/00 ,  G05B13/02 ,  G06F17/00 ,  G06F11/00 ,  H01L21/00 ,  G06F7/60 ,  G06F17/50 ,  G06F11/16 ,  G06F11/22 ,  G01R31/28 ,  H01L21/66

    CPC分类号： G01R31/2831 ,  H01L22/14 ,  H01L22/20 ,  H01L2924/0002 ,  H01L2924/00

    摘要： The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.

    摘要翻译： 本发明一般涉及半导体制造环境的监视和控制，更具体地说，涉及基于具有异构相关性的多个工具的数据的虚拟气象（VM）应用的方法和系统。 该方法和系统利用了多种工具的自然关系，利用嵌入在过程变量中的关系来提高VM预测晶圆质量建模的预测性能。 方法和系统的预测结果可以用作实际计量样本的替代或与实际计量样本结合，以便监测和控制半导体制造环境，从而减少与获得实际物理测量相关联的延迟和成本。

    Method and System for Wafer Quality Predictive Modeling based on Multi-Source Information with Heterogeneous Relatedness
    2.
    发明申请
    Method and System for Wafer Quality Predictive Modeling based on Multi-Source Information with Heterogeneous Relatedness 有权
    基于具有异构相关性的多源信息的晶片质量预测建模方法与系统

    公开(公告)号：US20140107824A1

    公开(公告)日：2014-04-17

    申请号：US13677542

    申请日：2012-11-15

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Yada Zhu ,  Jingrui He ,  Robert Jeffrey Baseman

    IPC分类号： G06F19/00

    CPC分类号： G01R31/2831 ,  H01L22/14 ,  H01L22/20 ,  H01L2924/0002 ,  H01L2924/00

    摘要： The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.

    摘要翻译： 本发明一般涉及半导体制造环境的监视和控制，更具体地说，涉及基于具有异构相关性的多个工具的数据的虚拟气象（VM）应用的方法和系统。 该方法和系统利用了多种工具的自然关系，利用嵌入在过程变量中的关系来提高VM预测晶圆质量建模的预测性能。 方法和系统的预测结果可以用作实际计量样本的替代或与实际计量样本结合，以便监测和控制半导体制造环境，从而减少与获得实际物理测量相关联的延迟和成本。

    Run-to-Run Control Utilizing Virtual Metrology in Semiconductor Manufacturing
    3.
    发明申请
    Run-to-Run Control Utilizing Virtual Metrology in Semiconductor Manufacturing 有权
    半导体制造中使用虚拟计量的运行控制

    公开(公告)号：US20140031969A1

    公开(公告)日：2014-01-30

    申请号：US13671902

    申请日：2012-11-08

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Robert J. Baseman ,  Jingrui He ,  Emmanuel Yashchin ,  Yada Zhu

    IPC分类号： G06F19/00

    CPC分类号： H01L22/20 ,  G05B13/048 ,  G05B19/41875 ,  G05B2219/32182 ,  G05B2219/32187 ,  G05B2219/32194 ,  G05B2219/45031 ,  Y02P90/14 ,  Y02P90/22 ,  Y02P90/26 ,  Y02P90/86

    摘要： An apparatus for performing run-to-run control and sampling optimization in a semiconductor manufacturing process includes at least one control module. The control module is operative: to determine a process output and corresponding metrology error associated with an actual metrology for a current processing run in the semiconductor manufacturing process; to determine a predicted process output and corresponding prediction error associated with a virtual metrology for the current processing run; and to control at least one parameter corresponding to a subsequent processing run as a function of the metrology error and the prediction error.

    摘要翻译： 一种用于在半导体制造过程中执行跑步运行控制和采样优化的装置包括至少一个控制模块。 控制模块可操作：确定与半导体制造过程中的当前处理运行的实际计量相关联的过程输出和相应的度量误差; 以确定与当前处理运行的虚拟计量相关联的预测过程输出和相应的预测误差; 并且根据计量误差和预测误差来控制与随后的处理运行相对应的至少一个参数。

    EXPLAINING OUTLIERS IN TIME SERIES AND EVALUATING ANOMALY DETECTION METHODS
    4.
    发明申请
    EXPLAINING OUTLIERS IN TIME SERIES AND EVALUATING ANOMALY DETECTION METHODS 有权

    公开(公告)号：US20220253426A1

    公开(公告)日：2022-08-11

    申请号：US17170164

    申请日：2021-02-08

    申请人： International Business Machines Corporation ,  The Board of Trustees of the University of Illinois

    发明人： Yada Zhu ,  Jinjun Xiong ,  Jingrui He ,  Lecheng Zheng ,  Xiaodong Cui

    IPC分类号： G06F16/23 ,  G06N3/04 ,  G06N3/08

    摘要： Time series data can be received. A machine learning model can be trained using the time series data. A contaminating process can be estimated based on the time series data, the contaminating process including outliers associated with the time series data. A parameter associated with the contaminating process can be determined. Based on the trained machine learning model and the parameter associated with the contaminating process, a single-valued metric can be determined, which represents an impact of the contaminating process on the machine learning model's future prediction. A plurality of different outlier detecting machine learning models can be used to estimate the contaminating process and the single-valued metric can be determined for each of the plurality of different outlier detecting machine learning models. The plurality of different outlier detecting machine learning models can be ranked according to the associated single-valued metric.