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.

公开(公告)号：WO2021170325A1

公开(公告)日：2021-09-02

申请号：PCT/EP2021/051656

申请日：2021-01-26

Applicant: ASML NETHERLANDS B.V.

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

IPC: G03F7/20 ,  G05B19/418

Abstract: Described herein is a method comprising: determining a sequence of states of an object, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object; determining, based on at least one of the states within the sequence of states and the one or more future states, a process metric associated with the object, the process metric comprising an indication of whether processing requirements for the object are satisfied for individual states in the sequence of states; and initiating an adjustment to processing based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied.

公开(公告)号：WO2020156724A1

公开(公告)日：2020-08-06

申请号：PCT/EP2019/084923

申请日：2019-12-12

Applicant: ASML NETHERLANDS B.V.

Inventor： HASIBI, Faegheh ,  VAN DIJK, Leon, Paul ,  LARRANAGA, Maialen ,  YPMA, Alexander ,  VAN HAREN, Richard, Johannes, Franciscus

IPC: G06N3/04 ,  G06N3/08

Abstract: According to an aspect of the disclosure there is provided a method for predicting a property associated with a product unit. The method may comprise obtaining a plurality of data sets, wherein each of the plurality of data sets comprises data associated with a spatial distribution of a parameter across the product unit, representing each of the plurality of data sets as a multidimensional object, obtaining a convolutional neural network model trained with previously obtained multidimensional objects and properties of previous product units, and applying the convolutional neural network model to the plurality of multidimensional objects representing the plurality of data sets, to predict the property associated with the product unit.

公开(公告)号：EP3935448A1

公开(公告)日：2022-01-12

申请号：EP20703998.3

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

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

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.

公开(公告)号：EP4111262A1

公开(公告)日：2023-01-04

申请号：EP21701325.9

申请日：2021-01-26

Applicant: ASML Netherlands B.V.

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

IPC: G03F7/20 ,  G05B19/418

公开(公告)号：EP3872567A1

公开(公告)日：2021-09-01

申请号：EP20159192.2

申请日：2020-02-25

Applicant: ASML Netherlands B.V.

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

IPC: G03F7/20 ,  G05B19/418

Abstract: Described herein is a model free reinforcement learning system and method for determining a relationship between sequences of states of a wafer across multiple process steps and process yield for the wafer. The system and method are configured such that an optimal policy (e.g. sequence of through stack actions) is defined which produces optimum wafer yield and/or a lowest cost (in view of metrology time for example) processing process.

公开(公告)号：EP3712817A1

公开(公告)日：2020-09-23

申请号：EP19164072.1

申请日：2019-03-20

Applicant: ASML Netherlands B.V.

Inventor： HASIBI, Faegheh ,  VAN DIJK, Leon, Paul ,  LARRANAGA, Maialen ,  YPMA, Alexander ,  VAN HAREN, Richard Johannes Franciscus

IPC: G06N3/04 ,  G06N3/08

Abstract: According to an aspect of the disclosure there is provided a method for predicting a property associated with a product unit. The method may comprise obtaining a plurality of data sets, wherein each of the plurality of data sets comprises data associated with a spatial distribution of a parameter across the product unit, representing each of the plurality of data sets as a multidimensional object, obtaining a convolutional neural network model trained with previously obtained multidimensional objects and properties of previous product units, and applying the convolutional neural network model to the plurality of multidimensional objects representing the plurality of data sets, to predict the property associated with the product unit.