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公开(公告)号：WO2023280493A1
公开(公告)日：2023-01-12
申请号：PCT/EP2022/065403
申请日：2022-06-07
Applicant: ASML NETHERLANDS B.V.
Inventor： VAN HERTUM, Pieter , GKOROU, Dimitra , VAN SCHOUBROECK, Joachim, Kinley , KAREVAN, Zahra , YPMA, Alexander
Abstract: A computer implemented method for diagnosing a system comprising a plurality of modules. The method comprises: receiving a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of the system, wherein each module is characterized by one or more signals; and (ii) edges connected between the nodes, the edges representing propagation of performance between modules; generating a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules; obtaining a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module; and using the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour.
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公开(公告)号：EP4449203A1
公开(公告)日：2024-10-23
申请号：EP22818084.0
申请日：2022-11-21
Applicant: ASML Netherlands B.V.
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公开(公告)号：EP4116888A1
公开(公告)日：2023-01-11
申请号：EP21184240.6
申请日：2021-07-07
Applicant: ASML Netherlands B.V.
Inventor： VAN HERTUM, Pieter , GKOROU, Dimitra , VAN SCHOUBROECK, Joachim, Kinley , KAREVAN, Zahra , YPMA, Alexander
Abstract: A computer implemented method for diagnosing a system comprising a plurality of modules. The method comprises: receiving a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of the system , wherein each module is characterized by one or more signals; and (ii) edges connected between the nodes, the edges representing propagation of performance between modules; generating a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules; obtaining a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module; and using the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour.
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公开(公告)号：EP4194951A1
公开(公告)日：2023-06-14
申请号：EP21214040.4
申请日：2021-12-13
Applicant: ASML Netherlands B.V.
Inventor： DOUNAEV, Dimitriy , GKOROU, Dimitra , VAN HERTUM, Pieter , LIJFFIJT, Jefrey , VAN SHOUBROECK, Joachim Kinley , KAREVAN, Zahra , YPMA, Alexander
Abstract: A fault in a subject production apparatus which is suspected of being a deviating machine, is identified based on whether it is possible to train a machine learning model to distinguish between first sensor data derived from the subject production apparatus, and second sensor data derived from one or more other production apparatuses which are assumed to be behaving normally. Thus, the discriminative ability of the machine learning model is used as an indicator to discriminate between a faulty machine and the population of healthy machines.
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