METHOD AND APPARATUS FOR SCANNING A SAMPLE WITH A PROBE

    公开(公告)号:US20240426869A1

    公开(公告)日:2024-12-26

    申请号:US18698671

    申请日:2022-10-07

    Abstract: A method of measuring a sample with a probe including a cantilever mount, a cantilever extending from the cantilever mount to a free end, and a probe tip carried by the free end of the cantilever is disclosed. The method includes taking a series of measurements of a sidewall of the sample with the probe; and analysing the series of measurements to determine a characteristic of the sidewall. The measurements are taken during a measurement cycle that includes a pair of measurement drive phases. The measurement drive phases include first and second drive phases in which the probe is driven, respectively, down, then up, next to the sidewall. During one of the drive phases the probe tip interacts with the sidewall, and the series of measurements are taken by measuring an angle of the cantilever as the probe tip interacts with the sidewall during the one of the drive phases.

    SCANNING PROBE MICROSCOPE, INFORMATION PROCESSING METHOD, AND PROGRAM

    公开(公告)号:US20240418746A1

    公开(公告)日:2024-12-19

    申请号:US18696244

    申请日:2022-03-24

    Abstract: A scanning probe microscope is equipped with an observation device for observing a sample containing particles and an information processing device. The information processing device generates one or more observation images based on observation data acquired by observing a sample with the observation device, calculates a particle parameter indicating a diameter of a particle image or the number of the particle images, the particle image being included in the observation image, and executes predetermined processing when an observation image including an image in which the particle parameter is outside a predefined observation range is included in one or more observation images.

    DEVICE AND METHOD FOR OPERATING A BENDING BEAM IN A CLOSED CONTROL LOOP

    公开(公告)号:US20240230709A1

    公开(公告)日:2024-07-11

    申请号:US18616453

    申请日:2024-03-26

    CPC classification number: G01Q30/04 G01Q10/065

    Abstract: The present invention relates to a device for operating at least one bending beam in at least one closed control loop, wherein the device has: (a) at least one first interface designed to receive at least one controlled variable of the at least one control loop; (b) at least one programmable logic circuit designed to process a control error of the at least one control loop using a bit depth greater than the bit depth of the controlled variable; and (c) at least one second interface designed to provide a manipulated variable of the at least one control loop.

    Atomic-force Microscopy for Identification of Surfaces

    公开(公告)号:US20240012022A1

    公开(公告)日:2024-01-11

    申请号:US18370923

    申请日:2023-09-21

    CPC classification number: G01Q30/04

    Abstract: A method comprises using an atomic-force microscope, acquiring a set of images associated with surfaces, and, using a machine-learning algorithm applied to the images, classifying the surfaces. As a particular example, the classification can be done in a way that relies on surface parameters derived from the images rather than using the images directly.

    Device and method for operating a bending beam in a closed control loop

    公开(公告)号:US11630124B2

    公开(公告)日:2023-04-18

    申请号:US17400349

    申请日:2021-08-12

    Abstract: The present invention relates to a device for operating at least one bending beam in at least one closed control loop, wherein the device has: (a) at least one first interface designed to receive at least one controlled variable of the at least one control loop; (b) at least one programmable logic circuit designed to process a control error of the at least one control loop using a bit depth greater than the bit depth of the controlled variable; and (c) at least one second interface designed to provide a manipulated variable of the at least one control loop.

    DEVICE AND METHOD FOR COMPREHENSIVE CHARACTERIZATION, ANALYSIS, HETERO-GENITY AND PURITY QUANTIFICATION OF EXTRACELLULAR VESICLES

    公开(公告)号:US20230003762A1

    公开(公告)日:2023-01-05

    申请号:US17887532

    申请日:2022-08-15

    Abstract: An extracellular vesicle characterization and analysis device in terms of their size, phenotype, and cargo content is provided. A method performed with the device to quantify the heterogeneity of extracellular vesicle samples both in terms of size and cargo content and further quantify the purity of extracellular vesicles based on their phenotype and cargo content is further provided. The extracellular vesicle characterization and analysis device includes an atomic force microscope and confocal Raman spectrometer subsystems that will present the phenotypic characterization and cargo analysis of extracellular vesicles, respectively. By processing the topographic images obtained by atomic force microscopy with image processing methods and analyzing them, the dimensional heterogeneity of the extracellular vesicle samples can be quantified and information about their purity can be presented. The confocal Raman spectrometer applies the tip-enhanced Raman spectrum method, performs a heterogeneity quantification and provides data on the purity of the sample.

    AUTOMATED ATOMIC SCALE FABRICATION
    10.
    发明申请

    公开(公告)号:US20220130033A1

    公开(公告)日:2022-04-28

    申请号:US17429443

    申请日:2020-02-14

    Abstract: A method for autonomously applying a dangling bond pattern to a substrate for atom scale device fabrication includes inputting the pattern, initiating a patterning process, scanning the substrate using a scanning probe microscope (SPM) to generate an SPM image of the substrate, feeding the SPM image into a trained convolution neural network (CNN), analyzing the SPM image using the CNN to identify substrate defects, determining a defect free substrate area for pattern application; and applying the pattern to the substrate in that area. An atom scale electronic component includes functional patches on a substrate and wires electrically connecting the functional patches. Training a CNN includes recording a Scanning Tunneling Microscope (STM) image of the substrate, extracting images of defects from the STM image, labeling pixel-wise the defect images, and feeding the extracted and labeled images of defects into a CNN to train the CNN for semantic segmentation.

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