公开(公告)号：WO2022144204A1

公开(公告)日：2022-07-07

申请号：PCT/EP2021/086782

申请日：2021-12-20

Applicant: ASML NETHERLANDS B.V.

Inventor： ONOSE, Alexandru ,  TIEMERSMA, Bart, Jacobus, Martinus ,  VERHEUL, Nick ,  DIRKS, Remco ,  BARBIERI, Davide ,  VAN LAARHOVEN, Hendrik, Adriaan

IPC: G06N3/04 ,  G06N3/08 ,  G01B11/00 ,  G03F7/20 ,  H01L21/66 ,  G01B11/02 ,  G01B11/06

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：WO2023083564A1

公开(公告)日：2023-05-19

申请号：PCT/EP2022/078803

申请日：2022-10-17

Applicant: ASML NETHERLANDS B.V.

Inventor： BARBIERI, Davide ,  CERFONTAINE, Pascal

IPC: G06N3/0455 ,  G06N3/096 ,  G03F7/20

Abstract: Autoencoder models may be used in the field of lithography to estimate, infer or predict a parameter of interest (e.g., metrology metrics). An autoencoder model is trained to predict a parameter by training it with measurement data (e.g., pupil images) of a substrate obtained from a measurement tool (e.g., optical metrology tool). Disclosed are methods and systems for synchronizing two or more autoencoder models for in-device metrology. Synchronizing two autoencoder models may configure the encoders of both autoencoder models to map from different signal spaces (e.g., measurement data obtained from different machines) to the same latent space, and the decoders to map from the same latent space to each autoencoder's respective signal space. Synchronizing may be performed for various purposes, including matching a measurement performance of one tool with another tool, and configuring a model to adapt to measurement process changes (e.g., changes in characteristics of the tool) over time.

公开(公告)号：EP4254266A1

公开(公告)日：2023-10-04

申请号：EP22164893.4

申请日：2022-03-29

Applicant: ASML Netherlands B.V.

Inventor： ONOSE, Alexandru ,  VERHEUL, Nick ,  TIEMERSMA, Bart, Jacobus, Martinus ,  CERFONTAINE, Pascal ,  BARBIERI, Davide

IPC: G06N3/04 ,  G06N3/08 ,  G03F7/20

Abstract: A method for ordering and/or selection of latent elements for modeling low dimensional data within a latent space representation, the low dimensional data being a reduced dimensionality representation of input data as determined by a first model component of a model, comprising the steps of training said model and selecting one of said latent element selections based on said training, said training comprising: reducing a dimensionality of the input data to generate said low dimensional data in said latent space representation; training a second model component of said model for each of one or more latent element selections; and optimizing an approximation of the input data as output by said second model component for each said latent element selection, thereby ranking at least one of said plurality of latent elements in the latent space representation based on a contribution of each latent element to the input data.

公开(公告)号：EP4181018A1

公开(公告)日：2023-05-17

申请号：EP21208063.4

申请日：2021-11-12

Applicant: ASML Netherlands B.V.

Inventor： BARBIERI, Davide ,  CERFONTAINE, Pascal

IPC: G06N3/04 ,  G06N3/08 ,  G03F7/20

Abstract: Autoencoder models may be used in the field of lithography to estimate, infer or predict a parameter of interest (e.g., metrology metrics). An autoencoder model is trained to predict a parameter by training it with measurement data (e.g., pupil images) of a substrate obtained from a measurement tool (e.g., optical metrology tool). Disclosed are methods and systems for synchronizing two or more autoencoder models for in-device metrology. Synchronizing two autoencoder models may configure the encoders of both autoencoder models to map from different signal spaces (e.g., measurement data obtained from different machines) to the same latent space, and the decoders to map from the same latent space to each autoencoder's respective signal space. Synchronizing may be performed for various purposes, including matching a measurement performance of one tool with another tool, and configuring a model to adapt to measurement process changes (e.g., changes in characteristics of the tool) over time.