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    METHODS & APPARATUS FOR CONTROLLING AN INDUSTRIAL PROCESS
    1.
    发明申请
    METHODS & APPARATUS FOR CONTROLLING AN INDUSTRIAL PROCESS 审中-公开
    用于控制工业过程的方法和设备

    公开(公告)号：WO2018072962A1

    公开(公告)日：2018-04-26

    申请号：PCT/EP2017/073868

    申请日：2017-09-21

    Applicant: ASML NETHERLANDS B.V.

    Inventor： KOU, Weitian ,  YPMA, Alexander ,  HAUPTMANN, Marc ,  KUPERS, Michiel ,  HAN, Min-Sub

    IPC: G05B19/418

    Abstract: A lithographic process is performed on a plurality of semiconductor wafers (900; 1020). The method includes selecting one or more of the wafers as sample wafers (910-914; 1030-1034). Metrology steps (922; 1042) are performed only on the selected sample wafers. Based on metrology results (924; 046) of the selected sample product units corrections are defined for use in controlling processing of the wafers or future wafers. The selection of sample product units is based at least partly on statistical analysis of object data (902; 1006) measured in relation to the wafers. The same object data or other data can be used for grouping wafers into groups. Selecting of sample wafers can include selecting wafers (910-914; 1030-1034) that are identified by said statistical analysis as most representative of the wafers in their group. The selecting of sample wafers can include elimination of product units (916; 036) that are identified as unrepresentative.

    Abstract translation: 在多个半导体晶片（900; 1020）上执行光刻工艺。 该方法包括选择一个或多个晶片作为样品晶片（910-914; 1030-1034）。 度量步骤（922; 1042）仅在所选样品晶圆上执行。 基于所选择的样品产品单元的计量结果（924; 046），定义校正用于控制晶片或未来晶片的处理。 样本产品单元的选择至少部分基于对晶片测量的对象数据（902; 1006）的统计分析。 可以使用相同的对象数据或其他数据将晶圆分组成组。 选择样本晶片可以包括选择由所述统计分析确定为最具代表性的晶片组的晶片（910-914; 1030-1034）。 选择样品晶片可以包括消除被确定为不具代表性的产品单元（916; 036）。

    METHOD TO LABEL SUBSTRATES BASED ON PROCESS PARAMETERS
    2.
    发明申请
    METHOD TO LABEL SUBSTRATES BASED ON PROCESS PARAMETERS 审中-公开

    公开(公告)号：WO2019206498A1

    公开(公告)日：2019-10-31

    申请号：PCT/EP2019/055262

    申请日：2019-03-04

    Applicant: ASML NETHERLANDS B.V.

    Inventor： BASTANI, Vahid ,  YPMA, Alexander

    IPC: G03F7/20 ,  G05B19/418

    Abstract: A method of grouping data associated with substrates undergoing a process step of a manufacturing process is disclosed. The method comprises obtaining first data associated with substrates before being subject to the process step and obtaining a plurality of sets of second data associated with substrates after being subject to the process step, each set of second data being associated with a different value of a characteristic of the first data. A distance metric is determined which describes a measure of distance between the sets of second data; and the second data is grouped based on a property of the distance metric.

    METHODS & APPARATUS FOR OBTAINING DIAGNOSTIC INFORMATION, METHODS & APPARATUS FOR CONTROLLING AN INDUSTRIAL PROCESS
    4.
    发明申请
    METHODS & APPARATUS FOR OBTAINING DIAGNOSTIC INFORMATION, METHODS & APPARATUS FOR CONTROLLING AN INDUSTRIAL PROCESS 审中-公开
    用于获得诊断信息的方法和设备，用于控制工业过程的方法和设备

    公开(公告)号：WO2018024466A1

    公开(公告)日：2018-02-08

    申请号：PCT/EP2017/067840

    申请日：2017-07-14

    Applicant: ASML NETHERLANDS B.V.

    Inventor： YPMA, Alexander ,  KOOPMAN, Adrianus, Cornelis, Matheus ,  MIDDLEBROOKS, Scott, Anderson

    IPC: G03F7/20 ,  H01L21/66 ,  G06K9/62

    CPC classification number: G03F7/70525 ,  G03F7/70508 ,  G03F7/70616 ,  G03F7/70625 ,  G03F7/70633 ,  G03F7/70641 ,  G05B19/41875 ,  G06K9/6296 ,  G06T7/001 ,  G06T2207/30148 ,  H01L22/20 ,  Y02P90/86

    Abstract: A diagnostic system (242, 244, 236, 248) implements a network comprising two or more sub-domains (DOM-A, B, C). Each sub-domain comprises diagnostic information extracted by analysis of object data, the first object data representing one or more first parameters measured in relation to a first set of product units that have been subjected nominally to the same industrial process as one another. The network further comprises at least one probabilistic connection (622, 624, 626) from a first variable in a first diagnostic sub-domain to a second variable in a second diagnostic sub-domain. Part of the second diagnostic information is thereby being influenced probabilistically by knowledge within the first diagnostic information. Diagnostic information may comprise for example a spatial fingerprint observed in the object data, or inferred. The network may include connections within sub- domains. The network may form a directed acyclic graph, and used for Bayesian inference operations.

    Abstract translation: 诊断系统（242,244,236,248）实现包括两个或更多个子域（DOM-A，B，C）的网络。 每个子域包括通过对象数据的分析而提取的诊断信息，第一对象数据表示关于第一组产品单位测量的一个或多个第一参数，第一组产品单位在名义上经历了彼此相同的工业过程。 网络还包括从第一诊断子域中的第一变量到第二诊断子域中的第二变量的至少一个概率连接（622,624,626）。 因此第二诊断信息的一部分由第一诊断信息内的知识概率性地受到影响。 诊断信息可以包括例如在对象数据中观察到的或推断的空间指纹。 网络可以包括子域内的连接。 网络可能形成有向无环图，并用于贝叶斯推理操作。

    EXTRACTING A FEATURE FROM A DATA SET
    5.
    发明申请
    EXTRACTING A FEATURE FROM A DATA SET 审中-公开

    公开(公告)号：WO2020177973A1

    公开(公告)日：2020-09-10

    申请号：PCT/EP2020/052953

    申请日：2020-02-06

    Applicant: ASML NETHERLANDS B.V.

    Inventor： LARRANAGA, Maialen ,  GKOROU, Dimitra ,  HASIBI, Faegheh ,  YPMA, Alexander

    IPC: G03F7/20 ,  G06N3/04

    Abstract: A method of extracting a feature from a data set includes iteratively extracting a feature (244) from a data set based on a visualization (238) of a residual pattern comprised within the data set, wherein the feature is distinct from a feature extracted in a previous iteration, and the visualization of the residual pattern uses the feature extracted in the previous iteration. Visualizing (234) the data set using the feature extracted in the previous iteration may comprise showing residual patterns of attribute data that are relevant to target data. Visualizing (234) the data set using the feature extracted in the previous iteration may involve adding cluster constraints to the data set, based on the feature extracted in the previous iteration. Additionally or alternatively, visualizing (234) the data set using the feature extracted in the previous iteration may involve defining conditional probabilities conditioned on the feature extracted in the previous iteration.

    METHODS & APPARATUS FOR OBTAINING DIAGNOSTIC INFORMATION RELATING TO AN INDUSTRIAL PROCESS
    8.
    发明申请
    METHODS & APPARATUS FOR OBTAINING DIAGNOSTIC INFORMATION RELATING TO AN INDUSTRIAL PROCESS 审中-公开
    用于获得与工业过程相关的诊断信息的方法和装置

    公开(公告)号：WO2015049087A1

    公开(公告)日：2015-04-09

    申请号：PCT/EP2014/068932

    申请日：2014-09-05

    Applicant: ASML NETHERLANDS B.V.

    Inventor： YPMA, Alexander ,  MENGER, Jasper ,  DECKERS, David ,  HAN, David ,  KOOPMAN, Adrianus ,  LYULINA, Irina ,  MIDDLEBROOKS, Scott ,  VAN HAREN, Richard ,  WILDENBERG, Jochem

    IPC: G03F7/20 ,  G03F9/00

    CPC classification number: G03F7/70616 ,  G03F7/70508 ,  G03F7/70525 ,  G03F7/70625 ,  G03F7/70633 ,  G03F7/70641 ,  G03F9/7092 ,  G06F17/30572

    Abstract: In a lithographic process product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each wafer. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose the set of said vectors representing the wafers in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using said component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

    Abstract translation: 在光刻工艺中，诸如半导体晶片的产品单元经受光刻图案化操作和化学和物理处理操作。 在执行过程期间分阶段进行对准数据或其他测量，以获得表示在每个晶片上空间分布的点处测量的位置偏差或其他参数的对象数据。 该对象数据用于通过执行多变量分析来获取诊断信息，以将表示所述多维空间中的晶片的所述矢量的集合分解为一个或多个分量向量。 使用所述分量向量提取关于工业过程的诊断信息。 可以基于提取的诊断信息来控制后续产品单元的工业过程的性能。

    METHOD AND APPARATUS FOR CONCEPT DRIFT MITIGATION
    9.
    发明申请
    METHOD AND APPARATUS FOR CONCEPT DRIFT MITIGATION 审中-公开

    公开(公告)号：WO2022028805A1

    公开(公告)日：2022-02-10

    申请号：PCT/EP2021/068888

    申请日：2021-07-07

    Applicant: ASML NETHERLANDS B.V.

    Inventor： KOULIERAKIS, Eleftherios ,  LANCIA, Carlo ,  GONZALEZ HUESCA, Juan Manuel ,  YPMA, Alexander

    IPC: G06N20/00 ,  G03F7/20 ,  G03F9/00

    Abstract: Method and apparatus for adapting a distribution model of a machine learning fabric. The distribution model is for mitigating the effect of concept drift, and is configured to provide an output as input to a functional model of the machine learning fabric. The functional model is for performing a machine learning task. The method comprises obtaining a first data point, and providing the first data point as input to one or more distribution monitoring components of the distribution model. The one or more distribution monitoring components have been trained on a plurality of further data points. A metric representing a correspondence between the first data point and the plurality of further data points is determined, by at least one of the one or more distribution monitoring components. Based on the error metric, the output of the distribution model is adapted.

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