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    Using convolution neural networks for on-the-fly single particle reconstruction
    3.
    发明授权
    Using convolution neural networks for on-the-fly single particle reconstruction 有权

    公开(公告)号：US11151356B2

    公开(公告)日：2021-10-19

    申请号：US16287982

    申请日：2019-02-27

    申请人： FEI Company

    发明人： John Flanagan ,  Erik Franken ,  Maurice Peemen

    IPC分类号： G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06T7/00 ,  G06T11/00 ,  G01N15/10

    摘要： Convolutional neural networks (CNNs) of a set of CNNs are evaluated using a test set of images (electron micrographs) associated with a selected particle type. A preferred CNN is selected based on the evaluation and used for processing electron micrographs of test samples. The test set of images can be obtained by manual selection or generated using a model of the selected particle type. Upon selection of images using the preferred CNN in processing additional electron micrographs, the selected images can be added to a training set or used as an additional training set to retrain the preferred CNN. In some examples, only selected layers of the preferred CNN are retrained. In other examples, two dimensional projections of based on particles of similar structure are used for CNN training or retraining.

    SPARSE IMAGE RECONSTRUCTION FROM NEIGHBORING TOMOGRAPHY TILT IMAGES
    4.
    发明申请
    SPARSE IMAGE RECONSTRUCTION FROM NEIGHBORING TOMOGRAPHY TILT IMAGES 有权

    公开(公告)号：US20220373481A1

    公开(公告)日：2022-11-24

    申请号：US17329081

    申请日：2021-05-24

    申请人： FEI Company

    发明人： Maurice Peemen ,  Holger Kohr ,  Pavel Potocek

    IPC分类号： G01N23/046 ,  G06T7/00 ,  G06T11/00 ,  G06T7/33

    摘要： Tomographic images are obtained by processing a tilt series of 2D images by aligning and combining images withing a group of neighbor images. The tilt series generally includes sparsely sampled images. Images of the tilt series at tilt angles associated with the sparsely sample images are selected as reference frames, grouped with neighbor images, and the group of images aligned. The aligned images are combined to produce replacement frames and a replacement frame tilt series that can be used for tomographic reconstruction.

    Depth reconstruction for 3D images of samples in a charged particle system
    5.
    发明授权
    Depth reconstruction for 3D images of samples in a charged particle system 有权

    公开(公告)号：US11380529B2

    公开(公告)日：2022-07-05

    申请号：US17039513

    申请日：2020-09-30

    申请人： FEI Company

    发明人： Pavel Potocek ,  Milo{hacek over (s)} Hovorka ,  Maurice Peemen ,  Luká{hacek over (s)} Hübner

    IPC分类号： H01J37/32 ,  H01J37/28

    摘要： Methods and systems for generating high resolution reconstructions of 3D samples imaged using slice and view processes where the electron interaction depth of the imaging beam is greater than slice thicknesses. Data obtained via such slice and view processes is enhanced with a depth blur reducing algorithm, that is configured to reduce depth blur caused by portions of the first data and second data that are resultant from electron interactions outside the first layer and second layer, respectively, to create enhanced first data and second enhanced data. A high-resolution 3D reconstruction of the sample is then generated using the enhanced first data and the enhanced second data. In some embodiments, the depth blur reducing algorithm may be selected from a set of such algorithms that have been individually configured for certain microscope conditions, sample conditions, or a combination thereof.

    DEEP LEARNING TECHNIQUES FOR FAST ANOMALY DETECTION IN EXPERIMENTAL DATA
    7.
    发明公开
    DEEP LEARNING TECHNIQUES FOR FAST ANOMALY DETECTION IN EXPERIMENTAL DATA 审中-公开

    公开(公告)号：US20240280522A1

    公开(公告)日：2024-08-22

    申请号：US18171541

    申请日：2023-02-20

    申请人： FEI COMPANY

    发明人： Hans Vanrompay ,  Maurice Peemen

    IPC分类号： G01N23/2252 ,  G06N3/0455

    CPC分类号： G01N23/2252 ,  G06N3/0455

    摘要： Disclosed herein are scientific instrument support systems, as well as related methods, apparatus, computing devices, and computer-readable media. Some embodiments provide a scientific instrument including detectors supporting one or more spectroscopic modalities and an imaging modality and further including an electronic controller configured to process streams of measurements received from the detectors. The electronic controller operates to generate a base image of the sample based on the measurements corresponding to the imaging modality and further operates to generate an anomaly map of the sample based on the base image and further based on differences between measured and autoencoder-reconstructed spectra corresponding to different pixels of the base image. In at least some instances, the anomaly map can beneficially be used in a quality-control procedure to identify, within seconds, specific problem spots in the sample for more-detailed inspection and/or analyses.

    Acquisition strategy for neural network based image restoration
    9.
    发明授权
    Acquisition strategy for neural network based image restoration 有权

    公开(公告)号：US11100612B2

    公开(公告)日：2021-08-24

    申请号：US16405536

    申请日：2019-05-07

    申请人： FEI Company

    发明人： Maurice Peemen ,  Pavel Poto{hacek over (c)}ek ,  Remco Schoenmakers

    IPC分类号： G06T5/00 ,  G06N3/08

    摘要： Methods and systems for neural network based image restoration are disclosed herein. An example method at least includes acquiring a plurality of training image pairs of a sample, where each training image of each of the plurality of training image pairs are images of a same location of a sample, and where each image of the plurality of training image pairs are acquired using same acquisition parameters, updating an artificial neural network based on the plurality of training image pairs, and denoising a plurality of sample images using the updated artificial neural network, where the plurality of sample images are acquired using the same acquisition parameters as used to acquire the plurality of training image pairs.