CHARGED PARTICLE BEAM WRITING APPARATUS, DISCHARGE DETECTION METHOD, AND DISCHARGE DETECTION APPARATUS

    公开(公告)号:US20250037969A1

    公开(公告)日:2025-01-30

    申请号:US18734266

    申请日:2024-06-05

    Abstract: In one embodiment, a charged particle beam writing apparatus includes a high-voltage power supply configured to apply an acceleration voltage for charged particles to an emission unit, a deflector configured to deflect a charged particle beam emitted from the emission unit, a discharge sensor configured to output an output value indicating a change in a case where a discharge occurs in a column in which the emission unit and the deflector are arranged or in the high-voltage power supply, and a discharge detection control unit configured to convert the output value from the discharge sensor into a conversion value with the same sign, calculate an integral value of the conversion value over a fixed period of time, and determine, based on the integral value, whether or not a discharge that causes a pattern error has occurred.

    Systems and methods for voltage contrast defect detection

    公开(公告)号:US12196692B2

    公开(公告)日:2025-01-14

    申请号:US17769690

    申请日:2020-09-30

    Abstract: Systems and methods of providing a probe spot in multiple modes of operation of a charged-particle beam apparatus are disclosed. The method may comprise activating a charged-particle source to generate a primary charged-particle beam and selecting between a first mode and a second mode of operation of the charged-particle beam apparatus. In the flooding mode, the condenser lens may focus at least a first portion of the primary charged-particle beam passing through an aperture of the aperture plate to form a second portion of the primary charged-particle beam, and substantially all of the second portion is used to flood a surface of a sample. In the inspection mode, the condenser lens may focus a first portion of the primary charged-particle beam such that the aperture of the aperture plate blocks off peripheral charged-particles to form the second portion of the primary charged-particle beam used to inspect the sample surface.

    CHARGED PARTICLE BEAM DEVICES AND MEMBRANE ASSEMBLIES USEFUL THEREIN

    公开(公告)号:US20240412940A1

    公开(公告)日:2024-12-12

    申请号:US18332969

    申请日:2023-06-12

    Abstract: A charged particle beam device, comprising a charged particle beam source situated in a first-pressure environment, a sample support operative to support a sample situated in a second-pressure environment, the second-pressure environment having a higher pressure than the first-pressure environment, and a membrane assembly separating the first-pressure environment from the second-pressure environment, the membrane assembly comprising a pressure-sealing membrane being substantially transparent to a charged particle beam from the charged particle beam source, a supporting membrane layer being formed with a cornerless aperture, the pressure-sealing membrane being bonded to the supporting membrane layer, and a holding frame being formed with a second aperture larger than and overlying the cornerless aperture. The charged particle beam device may further comprise an electron-detecting subassembly, the electron-detecting subassembly comprising at least one metal line defining a shape, for detection of electrons resulting from an interaction of the charged particle beam and the sample.

    FEW-SHOT LEARNING FOR PROCESSING MICROSCOPY IMAGES

    公开(公告)号:US20240354924A1

    公开(公告)日:2024-10-24

    申请号:US18303446

    申请日:2023-04-19

    Applicant: FEI Company

    Abstract: Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a scientific instrument support apparatus may include: first logic to receive, from a charged particle microscope, a microscopy image of a sample; second logic to generate a first processed image by processing the microscopy image through a general machine-learning model trained using a plurality of previously processed microscopy images; third logic to retrain the general machine-learning model with a related microscopy image, wherein the related microscopy image includes a label of an object related to the sample and the related microscopy image is not included in the plurality of previously processed microscopy images; and fourth logic to generate a second processed image, different from the first processed image, by processing the microscopy image through the retrained general machine-learning model.

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