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    CHARGED PARTICLE DEVICE AND METHOD
    124.
    发明公开
    CHARGED PARTICLE DEVICE AND METHOD 审中-公开

    公开(公告)号：US20240087835A1

    公开(公告)日：2024-03-14

    申请号：US18517642

    申请日：2023-11-22

    Applicant: ASML Netherlands B.V.

    Inventor： Albertus Victor Gerardus MANGNUS

    IPC: H01J37/05 ,  H01J37/12 ,  H01J37/21

    CPC classification number: H01J37/05 ,  H01J37/12 ,  H01J37/21 ,  H01J2237/036 ,  H01J2237/04756 ,  H01J2237/151 ,  H01J2237/216

    Abstract: The present disclosure provides a charged particle optical device for a charged particle system. The device projects an array of charged particle beams towards a sample. The device comprises a control lens array to control a parameter of the array of beams; and an objective lens array to project the array of beams onto the sample, the objective lens array being down beam of the control lens. The objective lens array comprises: an upper electrode; and a lower electrode arrangement that comprises an up-beam electrode and a down-beam electrode. The device is configured to apply an upper potential to the upper electrode, an up-beam potential to the up-beam electrode and a down-beam potential to the down-beam electrode. The potentials are controlled to control the landing energy of the beams on the sample and. to maintain focus of the beams on the sample at the landing energies.

    DUAL FOCUS SOLUTON FOR SEM METROLOGY TOOLS
    127.
    发明公开
    DUAL FOCUS SOLUTON FOR SEM METROLOGY TOOLS 审中-公开

    公开(公告)号：US20240071713A1

    公开(公告)日：2024-02-29

    申请号：US18270707

    申请日：2021-12-09

    Applicant: ASML Netherlands B.V.

    Inventor： Niels Johannes Maria BOSCH ,  Xu WANG ,  Peter Paul HEMPENIUS ,  Yongqiang WANG ,  Hans BUTLER ,  Youjin WANG ,  Jasper Hendrik GRASMAN ,  Jianzi SUI ,  Tianming CHEN ,  Aimin WU

    IPC: H01J37/20

    CPC classification number: H01J37/20 ,  H01J37/28 ,  H01J2237/20235

    Abstract: There is provided a charged particle apparatus comprising: a particle beam generator, optics, a first and a second positioning device, both configured for positioning the substrate relative to the particle beam generator along its optical axis, and a controller configured for switching between a first operational mode and a second operational mode. The apparatus is configured, when operating in the first operational mode, for irradiating the substrate by the particle beam at a first landing energy of the particle beam and, when operating in the second operational mode, for irradiating the substrate at a second, different landing energy. When operating in the first operational mode, the second positioning device is configured to position the substrate relative to the particle beam generator at a first focus position of the particle beam and in the second operational mode, to position the substrate at a second, different focus position.

    TRAINING MACHINE LEARNING MODELS BASED ON PARTIAL DATASETS FOR DEFECT LOCATION IDENTIFICATION
    128.
    发明公开
    TRAINING MACHINE LEARNING MODELS BASED ON PARTIAL DATASETS FOR DEFECT LOCATION IDENTIFICATION 审中-公开

    公开(公告)号：US20240069450A1

    公开(公告)日：2024-02-29

    申请号：US18267734

    申请日：2021-12-08

    Applicant: ASML Netherlands B.V.

    Inventor： Nabeel Noor MOIN ,  Chenxi LIN ,  Yi ZOU

    IPC: G03F7/00 ,  G06N20/20

    CPC classification number: G03F7/7065 ,  G03F7/706841 ,  G06N20/20

    Abstract: A method and apparatus for training a defect location prediction model to predict a defect for a substrate location is disclosed. A number of datasets having data regarding process-related parameters for each location on a set of substrates is received. Some of the locations have partial datasets in which data regarding one or more process-related parameters is absent. The datasets are processed to generate multiple parameter groups having data for different sets of process-related parameters. For each parameter group, a sub-model of the defect location prediction model is created based on the corresponding set of process-related parameters and trained using data from the parameter group. A trained sub-model(s) may be selected based on process-related parameters available in a candidate dataset and a defect prediction may be generated for a location associated with the candidate dataset using the selected sub-model.

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