公开(公告)号：EP3709082A1

公开(公告)日：2020-09-16

申请号：EP19162808.0

申请日：2019-03-14

Applicant: ASML Netherlands B.V.

Inventor： ONOSE, Alexandru ,  DIRKS, Remco ,  BOSCH, Roger, Hubertus, Elisabeth, Clementine ,  JACOBS, Sander, Silvester, Adelgondus, Marie ,  BUIJNSTERS, Frank, Jaco ,  DE ZWART, Siebe, Tjerk ,  PALHA DA SILVA CLERIGO, Artur

IPC: G03F7/20

Abstract: Disclosed are a method, computer program and associated apparatuses for metrology. The method includes determining a reconstruction recipe describing at least nominal values for using in a reconstruction of a parameterization describing a target. The method comprises obtaining first measurement data relating to measurements of a plurality of targets on at least one substrate, said measurement data relating to one or more acquisition settings and solving a cost function minimizing differences between the first measurement data and simulated measurement data based on a reconstructed parameterization for each of said plurality of targets. A constraint on the cost function is imposed based on a hierarchical prior. Also disclosed is a hybrid model for providing simulated data for use in reconstruction, comprising obtaining a coarse model operable to provide simulated coarse data; and training a data driven model to correct said simulated coarse data so as to determine said simulated data.

公开(公告)号：EP4075341A1

公开(公告)日：2022-10-19

申请号：EP21169035.9

申请日：2021-04-18

Applicant: ASML Netherlands B.V.

Inventor： ONOSE, Alexandru ,  TIEMERSMA, Bart, Jacobus, Martinus ,  VERHEUL, Nick ,  DIRKS, Remco

IPC: G06N3/04 ,  G06N3/08 ,  G01B11/00 ,  G03F7/20 ,  H01L21/66

Abstract: 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.

公开(公告)号：EP3961304A1

公开(公告)日：2022-03-02

申请号：EP20193721.6

申请日：2020-08-31

Applicant: ASML Netherlands B.V.

Inventor： DE ZWART, Siebe, Tjerk ,  DIRKS, Remco ,  NANDA, Gaurav ,  FAGGINGER AUER, Bastiaan, Onne

IPC: G03F7/20

Abstract: Methods and systems for determining a mapped intensity metric are described. Determining the mapped intensity metric comprises determining an intensity metric for a manufacturing system. The intensity metric is determined based on a reflectivity of a location on a substrate and a manufacturing system characteristic. Determining the mapped intensity metric also comprises determining a mapped intensity metric for a reference system. The reference system has a reference system characteristic. The mapped intensity metric is determined based on the intensity metric, the manufacturing system characteristic, and the reference system characteristic, to mimic the determination of the intensity metric for the manufacturing system using the reference system. In some embodiments, the reference system is virtual, and the manufacturing system is physical.

公开(公告)号：EP3611568A1

公开(公告)日：2020-02-19

申请号：EP18189181.3

申请日：2018-08-15

Applicant: ASML Netherlands B.V.

Inventor： MOSSAVAT, Seyed Iman ,  DIRKS, Remco ,  SMILDE, Hendrik Jan Hidde

IPC: G03F7/20

Abstract: A method of determining an estimated intensity of radiation scattered by a target illuminated by a radiation source, has the following steps: obtaining and training (402) a library REFLIB of wavelength-dependent reflectivity as a function of the wavelength, target structural parameters and angle of incidence K(λ,θ,x,y); determining (408) a wide-band library (W-BLIB) of integrals of wavelength-dependent reflectivity R of the target in a Jones framework over a range of radiation source wavelengths λ; training (TRN) (410) the wide-band library; and determining (412), using the trained wide-band library, an estimated intensity (INT) of radiation scattered by the target illuminated by the radiation source.

公开(公告)号：EP3514629A1

公开(公告)日：2019-07-24

申请号：EP18152891.0

申请日：2018-01-23

Applicant: ASML Netherlands B.V.

Inventor： BUIJNSTERS, Frank, Jaco ,  DIRKS, Remco ,  VAN LAARHOVEN, Hendrik, Adriaan

IPC: G03F7/20

Abstract: Disclosed is a method for constructing a parameterized geometric model of a structure, and an associated inspection apparatus. The method comprises determining an initial contour defining a modeled surface of the structure; and determining an output contour defining the modeled surface of the structure as a Minkowski sum of the initial contour and a kernel.

公开(公告)号：EP4075340A1

公开(公告)日：2022-10-19

申请号：EP21168592.0

申请日：2021-04-15

Applicant: ASML Netherlands B.V.

Inventor： TIEMERSMA, Bart, Jacobus, Martinus ,  ONOSE, Alexandru ,  VERHEUL, Nick ,  DIRKS, Remco

IPC: G06N3/04 ,  G06N3/08 ,  G01B11/00 ,  G03F7/20 ,  H01L21/66

Abstract: 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.

公开(公告)号：EP4075339A1

公开(公告)日：2022-10-19

申请号：EP21168585.4

申请日：2021-04-15

Applicant: ASML Netherlands B.V.

Inventor： ONOSE, Alexandru ,  TIEMERSMA, Bart Jacobus Martinus ,  VERHEUL, Nick ,  DIRKS, Remco

IPC: G06N3/04 ,  G06N3/08 ,  G01B11/00 ,  G03F7/20 ,  H01L21/66

Abstract: 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.

公开(公告)号：EP3637186A1

公开(公告)日：2020-04-15

申请号：EP18199371.8

申请日：2018-10-09

Applicant: ASML Netherlands B.V.

Inventor： MOSSAVAT, Seyed Iman ,  FAGGINGER AUER, Bastiaan Onne ,  DIRKS, Remco ,  ONOSE, Alexandru ,  CRAMER, Hugo Augustinus Joseph

IPC: G03F7/20

Abstract: Methods for calibrating metrology apparatuses and determining a parameter of interest are disclosed. In one arrangement, training data is provided that comprises detected representations of scattered radiation detected by each of plural metrology apparatuses. An encoder encodes each detected representation to provide an encoded representation, and a decoder generates a synthetic detected representation from the respective encoded representation. A classifier estimates from which metrology apparatus originates each encoded representation or each synthetic detected representation. The training data is used to simultaneously perform, in an adversarial relationship relative to each other, a first machine learning process involving the encoder or decoder and a second machine learning process involving the classifier.