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公开(公告)号：EP3705944A1
公开(公告)日：2020-09-09
申请号：EP19160933.8
申请日：2019-03-06
Applicant: ASML Netherlands B.V.
Inventor： LARRANAGA, Maialen , GKOROU, Dimitra , HASIBI, Faegheh , YPMA, Alexander
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.
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32.发明公开
公开(公告)号：EP4116888A1
公开(公告)日：2023-01-11
申请号：EP21184240.6
申请日：2021-07-07
Applicant: ASML Netherlands B.V.
Inventor： VAN HERTUM, Pieter , GKOROU, Dimitra , VAN SCHOUBROECK, Joachim, Kinley , KAREVAN, Zahra , YPMA, Alexander
Abstract: A computer implemented method for diagnosing a system comprising a plurality of modules. The method comprises: receiving a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of the system , wherein each module is characterized by one or more signals; and (ii) edges connected between the nodes, the edges representing propagation of performance between modules; generating a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules; obtaining a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module; and using the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour.
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公开(公告)号：EP3910419A1
公开(公告)日：2021-11-17
申请号：EP20174756.5
申请日：2020-05-14
Applicant: ASML Netherlands B.V.
Inventor： LANCIA, Carlo , GANTAPARA, Anjan Prasad , KERNKAMP, Dirk-Jan , MOSSAVAT, Seyed Iman , YPMA, Alexander
Abstract: Disclosed is a method of tuning a prediction model relating to at least one particular configuration of a manufacturing device. The method comprises obtaining a function comprising at least a first function of first prediction model parameters associated with said at least one particular configuration, and a second function of the first prediction model parameters and second prediction model parameters associated with configurations of the manufacturing device and/or related devices other than the at least one particular configuration. Values of the first prediction model parameters are obtained based on an optimization of the function, and a prediction model is tuned according to these values of the first prediction model parameters to obtain a tuned prediction mode.
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公开(公告)号：EP3352013A1
公开(公告)日：2018-07-25
申请号：EP17152659.3
申请日：2017-01-23
Applicant: ASML Netherlands B.V.
Inventor： YPMA, Alexander , GKOROU, Dimitra , TSIROGIANNIS, Georgios , HOOGENBOOM, Thomas, Leo, Maria , VAN HAREN, Richard, Johannes, Franciscus
IPC: G03F7/20 , G05B19/418 , H01L21/66
CPC classification number: G03F7/705 , G03F7/70525 , G03F7/70616 , G03F7/70625 , G03F7/70633 , G05B19/41875 , G05B2219/32194 , G05B2219/45028 , G05B2219/45031 , H01L22/10 , H01L22/12 , H01L22/20 , Y02P90/22
Abstract: The invention generates predicted data for control or monitoring of a production process to improve a parameter of interest. Context data 502 associated with operation of the production process 504 is obtained. Metrology/test 508 is performed on the product 506 of the production process 504, thereby obtaining performance data 510. A context-to-performance model is provided to generate predicted performance data 526 based on labeling of the context data 502 with performance data. This is an instance of semi-supervised learning. The context-to-performance model includes the learner 522 that performs semi-supervised labeling. The context-to-performance model is modified using prediction information related to quality of the context data and/or performance data. Prediction information may comprise relevance information relating to relevance of the obtained context data and/or obtained performance data to the parameter of interest. The prediction information may comprise model uncertainty information relating to uncertainty of the predicted performance data.
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公开(公告)号：EP3312693A1
公开(公告)日：2018-04-25
申请号：EP16195049.8
申请日：2016-10-21
Applicant: ASML Netherlands B.V.
Inventor： KOU, Weitian , YPMA, Alexander , HAUPTMANN, Marc , KUPERS, Michiel , HAN, Min-Sub
IPC: G05B19/418
CPC classification number: G05B19/41875 , G05B2219/2602 , G05B2219/32191 , G05B2219/32206 , G05B2219/37224 , G05B2219/42001 , G05B2219/45031 , Y02P90/22
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; 1046) 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; 1036) 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。 基于所选择的样本产品单元的计量结果（924; 1046），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识别为不具代表性的产品单元（916; 1036）。
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Patent Agency Ranking36.发明公开
公开(公告)号：EP3312672A1
公开(公告)日：2018-04-25
申请号：EP16195047.2
申请日：2016-10-21
Applicant: ASML Netherlands B.V.
Inventor： KOU, Weitian , YPMA, Alexander , HAUPTMANN, Marc , KUPERS, Michiel , VERGAIJ-HUIZER, Lydia, Marianna , WALLERBOS, Erik, Johannes, Maria , DELVIGNE, Erik, Henri, Adriaan
IPC: G03F20060101
CPC classification number: G03F7/70483 , G03F9/70
Abstract: Disclosed is a method of determining a correction for a process parameter related to a lithographic process on a substrate and associated apparatuses. The lithographic process comprises a plurality of runs during each one of which a pattern is applied to one or more substrates. The method comprises obtaining pre-exposure metrology data describing a property of the substrate; obtaining post-exposure metrology data comprising one or more measurements of the process parameter having been performed on one or more previously exposed substrates; assigning to the substrate, a class membership status from a plurality of classes, based on said pre-exposure metrology data; and determining the correction for the process parameter based on said class membership status and said post-exposure metrology data.
Abstract translation: 公开了一种确定与基板和相关装置上的光刻工艺有关的工艺参数的校正的方法。 光刻过程包括在其中每一个过程中将图案施加到一个或多个衬底的多次运行。 该方法包括获得描述衬底特性的预曝光度量数据; 获得曝光后测量数据，所述曝光后测量数据包括已经在一个或多个先前曝光的基板上执行的所述处理参数的一个或多个测量值; 基于所述预曝光度量数据，从多个类别向所述衬底分配类别成员资格状态; 以及基于所述类别成员状态和所述曝光后测量数据来确定对所述过程参数的校正。
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公开(公告)号：EP4194951A1
公开(公告)日：2023-06-14
申请号：EP21214040.4
申请日：2021-12-13
Applicant: ASML Netherlands B.V.
Inventor： DOUNAEV, Dimitriy , GKOROU, Dimitra , VAN HERTUM, Pieter , LIJFFIJT, Jefrey , VAN SHOUBROECK, Joachim Kinley , KAREVAN, Zahra , YPMA, Alexander
Abstract: A fault in a subject production apparatus which is suspected of being a deviating machine, is identified based on whether it is possible to train a machine learning model to distinguish between first sensor data derived from the subject production apparatus, and second sensor data derived from one or more other production apparatuses which are assumed to be behaving normally. Thus, the discriminative ability of the machine learning model is used as an indicator to discriminate between a faulty machine and the population of healthy machines.
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38.发明公开
公开(公告)号：EP4115241A1
公开(公告)日：2023-01-11
申请号：EP21702283.9
申请日：2021-02-04
Applicant: ASML Netherlands B.V.
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公开(公告)号：EP3961518A1
公开(公告)日：2022-03-02
申请号：EP20192534.4
申请日：2020-08-25
Applicant: ASML Netherlands B.V.
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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公开(公告)号：EP3785077A1
公开(公告)日：2021-03-03
申请号：EP19708314.0
申请日：2019-03-04
Applicant: ASML Netherlands B.V.
Inventor： BASTANI, Vahid , YPMA, Alexander
IPC: G03F7/20 , G05B19/418
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