公开(公告)号：US20190204180A1

公开(公告)日：2019-07-04

申请号：US16214196

申请日：2018-12-10

Applicant: ASML NETHERLANDS B.V.

Inventor： Mariya Vyacheslavivna MEDVEDYEVA ,  Maria Isabel DE LA FUENTE VALENTIN ,  Satej Subhash KHEDEKAR ,  Bert VERSTRAETEN ,  Bastiaan Onne FAGGINGER AUER

IPC: G01M11/02 ,  G01B11/00

CPC classification number: G01M11/02 ,  G01B11/002 ,  G01B2210/56 ,  G03F7/705 ,  G03F7/70625 ,  G03F7/70633

Abstract: Methods for processing data from a metrology process and for obtaining calibration data are disclosed. In one arrangement, measurement data is obtained from a metrology process. The metrology process includes illuminating a target on a substrate with measurement radiation and detecting radiation redirected by the target. The measurement data includes at least a component of a detected pupil representation of an optical characteristic of the redirected radiation in a pupil plane. The method further includes analyzing the at least a component of the detected pupil representation to determine either or both of a position property and a focus property of a radiation spot of the measurement radiation relative to the target.

公开(公告)号：US20250060738A1

公开(公告)日：2025-02-20

申请号：US18721460

申请日：2022-12-14

Applicant: ASML NETHERLANDS B.V.

Inventor： Eleftherios KOULIERAKIS ,  Anjan Prasad GANTAPARA ,  Satej Subhash KHEDEKAR ,  Hamideh ROSTAMI

IPC: G05B23/02

Abstract: A method for training a diagnostic model for diagnosing a production system, wherein the production system includes a plurality of sub-systems. The diagnostic model includes, for each sub-system, a corresponding first learning model arranged to receive input data, and to generate compressed data for the production system in a corresponding compressed latent space. A second learning model is arranged to receive the compressed data generated by the first learning models, and generate further compressed data for the production system in a further compressed latent space. The method includes performing training of the first and second learning models based on training data derived from sensor data characterizing the sub-systems.

公开(公告)号：US20230273529A1

公开(公告)日：2023-08-31

申请号：US18012222

申请日：2021-06-14

Applicant: ASML NETHERLANDS B.V.

Inventor： Satej Subhash KHEDEKAR ,  Henricus Jozef CASTELIJNS ,  Anjan Prasad GANTAPARA ,  Stephen Henry BOND ,  Seyed Iman MOSSAVAT ,  Alexander YPMA ,  Gerald DICKER ,  Ewout Klaas STEINMEIER ,  Chaoqun GUO ,  Chenxi LIN ,  Hongwei CHEN ,  Zhaoze LI ,  Youping ZHANG ,  Yi ZOU ,  Koos VAN BERKEL ,  Joost Johan BOLDER ,  Arnaud HUBAUX ,  Andriy Vasyliovich HLOD ,  Juan Manuel GONZALEZ HUESCA ,  Frans Bernard AARDEN

IPC: G03F7/20

CPC classification number: G03F7/70525 ,  G03F7/70633 ,  G03F7/7065

Abstract: Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input includes one or more parameters used in the patterning process. The control output is generated with a trained machine learning mod& based on the control input, The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data, The training data includes 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.