公开(公告)号：EP3480659A1

公开(公告)日：2019-05-08

申请号：EP17199539.2

申请日：2017-11-01

Applicant: ASML Netherlands B.V.

Inventor： ONOSE, Alexandru ,  MOSSAVAT, Seyed Iman ,  THEEUWES, Thomas

IPC: G03F7/20

Abstract: Methods and apparatus for estimating an unknown value of at least one of a plurality of sets of data, each set of data comprising 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 comprises at least one known value, and wherein at least one of the plurality of sets of data comprises an unknown value, the apparatus comprising a processor configured 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.

公开(公告)号：EP4502926A1

公开(公告)日：2025-02-05

申请号：EP23188908.0

申请日：2023-08-01

Applicant: ASML Netherlands B.V.

Inventor： BOTARI, Tiago ,  ONOSE, Alexandru ,  TRAJANOSKA, Marija ,  PISARENCO, Maxim ,  KIEHN, Moritz, Simon, Maria ,  KUIPER, Vincent, Sylvester

IPC: G06T7/00 ,  G06T7/194 ,  G01N23/2251 ,  G03F7/00 ,  G06V20/69 ,  H01L21/66

Abstract: A data processing method for image data obtained by scanning a charged particle beam across a sample; the method comprising:
classifying pixels of the image data into foreground pixels and background pixels to generate a foreground pixel map; and
encoding the foreground pixel map as encoded data using a sparse matrix encoding technique.

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：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.