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    METHOD AND APPARATUS FOR DETERMINING FEATURE CONTRIBUTION TO PERFORMANCE
    1.
    发明申请
    METHOD AND APPARATUS FOR DETERMINING FEATURE CONTRIBUTION TO PERFORMANCE 有权

    公开(公告)号：US20220351075A1

    公开(公告)日：2022-11-03

    申请号：US17624014

    申请日：2020-06-05

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Vahid BASTANI ,  Dag SONNTAG ,  Reza SAHRAEIAN ,  Dimitra GKOROU

    IPC: G06N20/00 ,  G03F7/20

    Abstract: A method of determining a contribution of a process feature to the performance of a process of patterning substrates. The method may include obtaining a first model trained on first process data and first performance data. One or more substrates may be identified based on a quality of prediction of the first model when applied to process data associated with the one or more substrates. A second model may be trained on second process data and second performance data associated with the identified one or more substrates. The second model may be used to determine the contribution of a process feature of the second process data to the second performance data associated with the one or more substrates.

    METHOD FOR CLASSIFYING SEMICONDUCTOR WAFERS
    2.
    发明公开
    METHOD FOR CLASSIFYING SEMICONDUCTOR WAFERS 审中-公开

    公开(公告)号：US20230316103A1

    公开(公告)日：2023-10-05

    申请号：US18013636

    申请日：2021-06-21

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Vahid BASTANI ,  Dimitra GKOROU ,  Reza SAHRAEIAN ,  Cyrus Emil TABERY

    IPC: G06N5/022 ,  H01L21/66

    CPC classification number: G06N5/022 ,  H01L22/20

    Abstract: Methods and apparatus for classifying semiconductor wafers. The method can include: sorting a set of semiconductor wafers, using a model, into a plurality of sub-sets based on parameter data corresponding to one or more parameters of the set of semiconductor wafers, wherein the parameter data for semiconductor wafers in a sub-set include one or more common characteristics; identifying one or more semiconductor wafers within a sub-set based on a probability of the one or more semiconductor wafers being correctly allocated to the sub-set; comparing the parameter data of the one or more identified semiconductor wafers to reference parameter data; and reconfiguring the model based on the comparison. The comparison is undertaken by a human to provide constraints for the model. The apparatus can be configured to undertake the method.

    EXTRACTING A FEATURE FROM A DATA SET
    3.
    发明申请
    EXTRACTING A FEATURE FROM A DATA SET 有权

    公开(公告)号：US20220128908A1

    公开(公告)日：2022-04-28

    申请号：US17436113

    申请日：2020-02-06

    Applicant: ASML NETHERLANDS B.V.

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

    IPC: G03F7/20 ,  G06N20/00

    Abstract: A method of extracting a feature from a data set includes iteratively extracting a feature from a data set based on a visualization of a residual pattern 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 the data set using the feature extracted in the previous iteration may include showing residual patterns of attribute data that are relevant to target data. Visualizing 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 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.

    ACTIVE LEARNING TO IMPROVE WAFER DEFECT CLASSIFICATION
    5.
    发明申请
    ACTIVE LEARNING TO IMPROVE WAFER DEFECT CLASSIFICATION 有权

    公开(公告)号：US20250117921A1

    公开(公告)日：2025-04-10

    申请号：US18832094

    申请日：2023-01-19

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Blagorodna ILIEVSKA ALCHEVA ,  Dimitra GKOROU ,  Harshil Jayantbhai LAKKAD ,  Artunç ULUCAN ,  Robin Theodorus Christiaan DE WIT

    IPC: G06T7/00 ,  G06V10/764

    Abstract: Systems and methods for training a machine learning model to classify defects with utility-function-based active learning are described. In one embodiment, one or more non-transitory, machine-readable mediums are configured to cause a processor to at least determine a utility function value for unclassified measurement images, based on a machine learning model, wherein the machine learning model is trained using a pool of labeled measurement images. Based on a determination that the utility function value for a given unclassified measurement image is less than a threshold value, the unclassified measurement image is output for classification without the use of the machine learning model. The unclassified measurement images classified via the classification without the use of the machine learning model are added to the pool of labeled measurement images. The machine learning model is trained based on the measurement images classified via the classification without the use of the machine learning model.

    COMPUTER IMPLEMENTED METHOD FOR DIAGNOSING A SYSTEM COMPRISING A PLURALITY OF MODULES
    6.
    发明公开
    COMPUTER IMPLEMENTED METHOD FOR DIAGNOSING A SYSTEM COMPRISING A PLURALITY OF MODULES 审中-公开

    公开(公告)号：US20240273278A1

    公开(公告)日：2024-08-15

    申请号：US18568115

    申请日：2022-06-07

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Pieter VAN HERTUM ,  Dimitra GKOROU ,  Joachim Kinley VAN SCHOUBROECK ,  Zahra KAREVAN ,  Alexander YPMA

    IPC: G06F30/398 ,  G03F7/00

    CPC classification number: G06F30/398 ,  G03F7/705 ,  G03F7/70508

    Abstract: A computer implemented method for diagnosing a system includes: receiving a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of a plurality of modules of a 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 behavior.

    CONFIGURATION OF AN IMPUTER MODEL
    7.
    发明公开
    CONFIGURATION OF AN IMPUTER MODEL 审中-公开

    公开(公告)号：US20230153582A1

    公开(公告)日：2023-05-18

    申请号：US17913305

    申请日：2021-03-22

    Applicant: ASML NETHERLANDS B.V.

    Inventor： Reza SAHRAEIAN ,  Vahid BASTANI ,  Dimitra GKOROU ,  Thiago DOS SANTOS GUZELLA

    IPC: G06N3/0475

    CPC classification number: G06N3/0475

    Abstract: Apparatus and methods of configuring an imputer model for imputing a second parameter. The method includes inputting a first data set including values of a first parameter to the imputer model, and evaluating the imputer model to obtain a second data set including imputed values of the second parameter. The method further includes obtaining a third data set including measured values of a third parameter, wherein the third parameter is correlated to the second parameter; obtaining a prediction model configured to infer values of the third parameter based on inputting values of the second parameter; inputting the second data set to the prediction model, and evaluating the prediction model to obtain inferred values of the third parameter; and configuring the imputer model based on a comparison of the inferred values and the measured values of the third parameter.

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