ON SYSTEM SELF-DIAGNOSIS AND SELF-CALIBRATION TECHNIQUE FOR CHARGED PARTICLE BEAM SYSTEMS

    公开(公告)号:US20240186106A1

    公开(公告)日:2024-06-06

    申请号:US18284839

    申请日:2022-03-03

    CPC classification number: H01J37/24 H01J37/244

    Abstract: An improved method of performing a self-diagnosis of a charged particle inspection system is disclosed. An improved method comprises triggering a self-diagnosis based on output data of the charged particle inspection system; in response to the triggering of the self-diagnosis, receiving diagnostic data of a sub-system of the charged particle inspection system; identifying an issue associated with the output data based on the diagnostic data of the sub-system; and generating a control signal to adjust an operation parameter of the sub-system according to the identified issue.

    METHOD AND APPARATUS FOR PATTERN FIDELITY CONTROL

    公开(公告)号:US20200019069A1

    公开(公告)日:2020-01-16

    申请号:US16468063

    申请日:2017-11-28

    Abstract: A method of topography determination, the method including: obtaining a first focus value derived from a computational lithography model modeling patterning of an unpatterned substrate or derived from measurements of a patterned layer on an unpatterned substrate; obtaining a second focus value derived from measurement of a substrate having a topography; and determining a value of the topography from the first and second focus values.

    DEFECT PREDICTION
    8.
    发明申请

    公开(公告)号:US20210150115A1

    公开(公告)日:2021-05-20

    申请号:US16629633

    申请日:2018-06-20

    Abstract: A method including obtaining verified values of a characteristic of a plurality of patterns on a substrate produced by a device manufacturing process; obtaining computed values of the characteristic using a non-probabilistic model; obtaining values of a residue of the non-probabilistic model based on the verified values and the computed values; and obtaining an attribute of a distribution of the residue based on the values of the residue. Also disclosed herein are methods of computing a probability of defects on a substrate produced by the device manufacturing process, and of obtaining an attribute of a distribution of the residue of a non-probabilistic model.

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