公开(公告)号：US20230252347A1

公开(公告)日：2023-08-10

申请号：US18015162

申请日：2021-07-07

Applicant: ASML NETHERLANDS B.V.

Inventor： Eleftherios KOULIERAKIS ,  Carlo LANCIA ,  Juan Manuel GONZALEZ HUESCA ,  Alexander YPMA

IPC: G06N20/00 ,  G03F7/00 ,  G03F7/20

CPC classification number: G06N20/00 ,  G03F7/706841 ,  G03F7/70516 ,  G03F7/705

Abstract: Method and apparatus for adapting a distribution model of a machine learning fabric. The distribution model is for mitigating the effect of concept drift, and is configured to provide an output as input to a functional model of the machine learning fabric. The functional model is for performing a machine learning task. The method may include obtaining a first data point, and providing the first data point as input to one or more distribution monitoring components of the distribution model. The one or more distribution monitoring components have been trained on a plurality of further data points. A metric representing a correspondence between the first data point and the plurality of further data points is determined, by at least one of the one or more distribution monitoring components. Based on the error metric, the output of the distribution model is adapted.

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：US20230058166A1

公开(公告)日：2023-02-23

申请号：US17910454

申请日：2021-03-01

Applicant: ASML NETHERLANDS B.V.

Inventor： Eleftherios KOULIERAKIS ,  Carlo LANCIA ,  Juan Manuel GONZALEZ HUESCA ,  Alexander YPMA ,  Dimitra GKOROU ,  Reza SAHRAEIAN

IPC: G03F7/20

Abstract: A method for determining an inspection strategy for at least one substrate, the method including: quantifying, using a prediction model, a compliance metric value for a compliance metric relating to a prediction of compliance with a quality requirement based on one or both of pre-processing data associated with the substrate and any available post-processing data associated with the at least one substrate; and deciding on an inspection strategy for the at least one substrate, based on the compliance metric value, an expected cost associated with the inspection strategy and at least one objective value describing an expected value of the inspection strategy in terms of at least one objective relating to the prediction model.