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公开(公告)号:US20240354552A1
公开(公告)日:2024-10-24
申请号:US18259344
申请日:2021-12-20
发明人: Alexandru ONOSE , Bart Jacobus Martinus TIEMERSMA , Nick VERHEUL , Remco DIRKS , Davide BARBIERI , Hendrik Adriaan VAN LAARHOVEN
IPC分类号: G06N3/0455
CPC分类号: G06N3/0455
摘要: A modular autoencoder model is described. The modular autoencoder model comprises input models configured to process one or more inputs to a first level of dimensionality suitable for combination with other inputs: a common model configured to: reduce a dimensionality of combined processed inputs to generate low dimensional data in a latent space; and expand the low dimensional data in the latent space into one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; output models configured to use the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs; and a prediction model configured to estimate one or more parameters based on the low dimensional data in the latent space.
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公开(公告)号:US20240060906A1
公开(公告)日:2024-02-22
申请号:US18270074
申请日:2021-12-20
IPC分类号: G01N21/95 , G03F7/00 , G06N3/0455 , G06N3/0895
CPC分类号: G01N21/9501 , G03F7/70625 , G03F7/706839 , G06N3/0455 , G06N3/0895
摘要: A modular autoencoder model is described. The modular autoencoder model comprises input models configured to process one or more inputs to a first level of dimensionality suitable for combination with other inputs; a common model configured to: reduce a dimensionality of combined processed inputs to generate low dimensional data in a latent space; and expand the low dimensional data in the latent space into one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; output models configured to use the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs; and a prediction model configured to estimate one or more parameters based on the low dimensional data in the latent space.
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公开(公告)号:US20190323972A1
公开(公告)日:2019-10-24
申请号:US16385651
申请日:2019-04-16
发明人: Samee Ur REHMAN , Anagnostis TSIATMAS , Sergey TARABRIN , Joannes Jitse VENSELAAR , Alexandru ONOSE , Mariya Vyacheslavivna MEDVEDYEVA
IPC分类号: G01N21/95 , G01N21/956
摘要: Methods of determining a value of a parameter of interest are disclosed. In one arrangement, a symmetric component and an asymmetric component of a detected pupil representation from illuminating a target are derived. A first metric characterizing the symmetric component and a second metric characterizing the asymmetric component vary non-monotonically as a function of the parameter of interest over a reference range of values of the parameter of interest. A combination of the derived symmetric component and the derived asymmetric component are used to identify a correct value from a plurality of candidate values of the parameter of interest.
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公开(公告)号:US20240061347A1
公开(公告)日:2024-02-22
申请号:US18259354
申请日:2021-12-20
IPC分类号: G03F7/00 , G01N21/95 , G06N3/0455 , G06N3/08
CPC分类号: G03F7/706839 , G01N21/9501 , G03F7/70616 , G06N3/0455 , G06N3/08
摘要: A modular autoencoder model is described. The modular autoencoder model comprises input models configured to process one or more inputs to a first level of dimensionality suitable for combination with other inputs; a common model configured to: reduce a dimensionality of combined processed inputs to generate low dimensional data in a latent space; and expand the low dimensional data in the latent space into one or more expanded versions of the one or more inputs suitable for generating one or more different outputs; output models configured to use the one or more expanded versions of the one or more inputs to generate the one or more different outputs, the one or more different outputs being approximations of the one or more inputs; and a prediction model configured to estimate one or more parameters based on the low dimensional data in the latent space.
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公开(公告)号:US20220171290A1
公开(公告)日:2022-06-02
申请号:US17436947
申请日:2020-02-26
发明人: Alexandru ONOSE , Remco DIRKS , Roger Hubertus Elisabeth Clementin BOSCH , Sander Silvester Adelgondus Marie JACOBS , Frank Jaco BUIJNSTERS , Siebe Tjerk DE ZWART , Artur PALHA DA SILVA CLERIGO , Nick VERHEUL
IPC分类号: G03F7/20
摘要: A method, computer program and associated apparatuses for metrology. The method includes determining a reconstruction recipe describing at least nominal values for use in a reconstruction of a parameterization describing a target. The method includes obtaining first measurement data relating to measurements of a plurality of targets on at least one substrate, the measurement data relating to one or more acquisition settings and performing an optimization by minimizing a cost function which minimizes differences between the first measurement data and simulated measurement data based on a reconstructed parameterization for each of the plurality of targets. A constraint on the cost function is imposed based on a hierarchical prior. Also disclosed is a hybrid model method comprising obtaining a coarse model operable to provide simulated coarse data; and training a data driven model to correct the simulated coarse data so as to determine simulated data for use in reconstruction.
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公开(公告)号:US20190129313A1
公开(公告)日:2019-05-02
申请号:US16163751
申请日:2018-10-18
IPC分类号: G03F7/20
CPC分类号: G03F7/70508 , G03F7/705 , G03F7/70608 , G03F7/70633 , G03F7/70666
摘要: Methods and apparatus for estimating an unknown value of at least one of a plurality of sets of data, each set of data including a plurality of values indicative of radiation diffracted and/or reflected and/or scattered by one or more features fabricated in or on a substrate, wherein the plurality of sets of data include at least one known value, and wherein at least one of the plurality of sets of data includes an unknown value, the apparatus including a processor to estimate the unknown value of the at least one set of data based on: the known values of the plurality of sets of data, a first condition between two or more values within a set of data of the plurality of sets of data, and a second condition between two or more values being part of different sets of data of the plurality of the sets of data.
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