Abstract: The present invention relates to a method of training a network for reconstructing and/or segmenting microscopic images comprising the step of training the network in the cloud. Further, for training the network in the cloud training data comprising microscopic images can be uploaded into the cloud and a network is trained by the microscopic images. Moreover, for training the network the network can be benchmarked after the reconstructing and/or segmenting of the microscopic images. Wherein for benchmarking the network the quality of the image(s) having undergone the reconstructing and/or segmenting by the network can be compared with the quality of the image(s) having undergone reconstructing and/or segmenting by already known algorithm and/or a second network.

Abstract: The present invention relates to a method of training a network for reconstructing and/or segmenting microscopic images comprising the step of training the network in the cloud. Further, for training the network in the cloud training data comprising microscopic images can be uploaded into the cloud and a network is trained by the microscopic images. Moreover, for training the network the network can be benchmarked after the reconstructing and/or segmenting of the microscopic images. Wherein for benchmarking the network the quality of the image(s) having undergone the reconstructing and/or segmenting by the network can be compared with the quality of the image(s) having undergone reconstructing and/or segmenting by already known algorithm and/or a second network.

Abstract: The present invention relates to a method of training a network for reconstructing and/or segmenting microscopic images comprising the step of training the network in the cloud. Further, for training the network in the cloud training data comprising microscopic images can be uploaded into the cloud and a network is trained by the microscopic images. Moreover, for training the network the network can be benchmarked after the reconstructing and/or segmenting of the microscopic images. Wherein for benchmarking the network the quality of the image(s) having undergone the reconstructing and/or segmenting by the network can be compared with the quality of the image(s) having undergone reconstructing and/or segmenting by already known algorithm and/or a second network.

Abstract: Producing and storing a first image, of a first, initial surface of the specimen; In a primary modification step, modifying said first surface, thereby yielding a second, modified surface; Producing and storing a second image, of said second surface; Using a mathematical Image Similarity Metric to perform pixel-wise comparison of said second and first images, so as to generate a primary figure of merit for said primary modification step.

Abstract: Methods and apparatuses for implementing artificial intelligence enabled volume reconstruction are disclosed herein. An example method at least includes acquiring a first plurality of multi-energy images of a surface of a sample, each image of the first plurality of multi-energy images obtained at a different beam energy, where each image of the first plurality of multi-energy images include data from a different depth within the sample, and reconstructing, by an artificial neural network, at least a volume of the sample based on the first plurality of multi-energy images, where a resolution of the reconstruction is greater than a resolution of the first plurality of multi-energy images.

Abstract: An x-ray source for computer tomography uses several sub-sources. An electron beam impacts the several sub-sources to achieve a high x-ray flux with high resolution. The several sub-sources produce a composite image, which is deconvolved to disentangle the composite image and render a useful image. The configuration of the several sub-sources can be optimized for a given specimen structure.

Abstract: The present invention relates to a method of training a network for reconstructing and/or segmenting microscopic images comprising the step of training the network in the cloud. Further, for training the network in the cloud training data comprising microscopic images can be uploaded into the cloud and a network is trained by the microscopic images. Moreover, for training the network the network can be benchmarked after the reconstructing and/or segmenting of the microscopic images. Wherein for benchmarking the network the quality of the image(s) having undergone the reconstructing and/or segmenting by the network can be compared with the quality of the image(s) having undergone reconstructing and/or segmenting by already known algorithm and/or a second network.

Abstract: Producing and storing a first image, of a first, initial surface of the specimen; In a primary modification step, modifying said first surface, thereby yielding a second, modified surface; Producing and storing a second image, of said second surface; Using a mathematical Image Similarity Metric to perform pixel-wise comparison of said second and first images, so as to generate a primary figure of merit for said primary modification step.

Abstract: A method of investigating a specimen using charged-particle microscopy, and a charged particle microscope configured for same.

Abstract: A method of investigating a specimen using a charged particle microscope, including: Producing and storing a first image, of a first, initial surface of the specimen; In a primary modification step, modifying said first surface, thereby yielding a second, modified surface; Producing and storing a second image, of said second surface; Using a mathematical Image Similarity Metric to perform pixel-wise comparison of said second and first images, so as to generate a primary figure of merit for said primary modification step.

Abstract: A Charged Particle Microscope includes A specimen holder, for holding a specimen; A source, for producing a beam of charged particles; An illuminator, for directing said beam so as to irradiate the specimen; and A detector, for detecting a flux of radiation emanating from the specimen in response to said irradiation. The illuminator includes: An aperture plate comprising an aperture region in a path of said beam, for defining a geometry of the beam prior to its impingement upon said specimen. The aperture region includes a distribution of multiple holes, each of which is smaller than a diameter of the beam incident on the aperture plate.

Abstract: A method of investigating a specimen using charged-particle microscopy, comprising the following steps: (a) On a surface of the specimen, selecting a virtual sampling grid extending in an XY plane and comprising grid nodes to be impinged upon by a charged-particle probing beam during a two-dimensional scan of said surface; (b) Selecting a landing energy Ei for said probing beam, with an associated nominal Z penetration depth di below said surface; (c) At each of said nodes, irradiating the specimen with said probing beam and detecting output radiation emanating from the specimen in response thereto, thereby generating a scan image Ii; (d) Repeating steps (b) and (c) for a series {Ei} of different landing energies, corresponding to an associated series {di} of different penetration depths, further comprising the following steps: (e) Pre-selecting an initial energy increment ?Ei by which Ei is to be altered after a first iteration of steps (b) and (c); (f) Associating energy increment ?Ei with a correspon

Abstract: A method of examining a sample using a charged-particle microscope, comprising mounting the sample on a sample holder; using a particle-optical column to direct at least one beam of particulate radiation onto a surface S of the sample, thereby producing an interaction that causes emitted radiation to emanate from the sample; using a detector arrangement to detect at least a portion of said emitted radiation, the method of which comprises embodying the detector arrangement to detect electrons in the emitted radiation; recording an output On of said detector arrangement as a function of kinetic energy En of said electrons, thus compiling a measurement set M={(On, En)} for a plurality of values of En; using computer processing apparatus to automatically deconvolve the measurement set M and spatially resolve it into a result set R={(Vk, Lk)}, in which a spatial variable V demonstrates a value Vk at an associated discrete depth level Lk referenced to the surface S, whereby n and k are members of an integer sequenc

Abstract: A method of imaging a specimen using an X-ray imaging apparatus, comprising the following steps: Providing the specimen on a specimen holder; Directing a flux of X-rays from a source through the specimen and onto an X-ray camera, which method additionally comprises the following steps: Embodying the source as a cluster of component sources, with a confined angular span relative to the specimen; Using said camera to record a cumulative, composite image from said component sources; Mathematically deconvolving said composite image.

Abstract: A method and apparatus for imaging a specimen using a scanning-type microscope, by irradiating a specimen with a beam of radiation using a scanning motion, and detecting a flux of radiation emanating from the specimen in response to the irradiation, in the first sampling session {S1} of a set {Sn}, gathering data from a first collection of sparsely distributed sampling points {P1} of set {Pn}. A mathematical registration correction is made to compensate for drift mismatches between different members of the set {Pn}, and an image of the specimen is assembled using the set {Pn} as input to an integrative mathematical reconstruction procedure.

Abstract: A method of investigating a specimen using a charged particle microscope, including: Producing and storing a first image, of a first, initial surface of the specimen; In a primary modification step, modifying said first surface, thereby yielding a second, modified surface; Producing and storing a second image, of said second surface; Using a mathematical Image Similarity Metric to perform pixel-wise comparison of said second and first images, so as to generate a primary figure of merit for said primary modification step.

Abstract: A method of imaging a specimen comprises directing a beam to irradiate a specimen; detecting radiation emanating from the specimen; scanning the beam along a path; for each sample point in said path, recording a measurement set M={(Dn, Pn)}, where Dn is the detector output as a function of value Pn of measurement parameter P; deconvolving M and spatially resolving it into a set representing depth-resolved imagery of the specimen, whereby, at point pi within the specimen, in a first probing session, irradiating, in a first beam configuration, pi with Point Spread Function F1, whereby said beam configuration is different to P; in at least a second probing session, irradiating, in a second beam configuration, pi with Point Spread Function F2 which overlaps partially with F1 in a zone Oi in which pi is located; sing an Independent Component Analysis algorithm to perform spatial resolution in Oi.

Abstract: A Charged Particle Microscope, comprising: includes A specimen holder, for holding a specimen; A source, for producing a beam of charged particles; An illuminator, for directing said beam so as to irradiate the specimen; and A detector, for detecting a flux of radiation emanating from the specimen in response to said irradiation. The illuminator includes: An aperture plate comprising an aperture region in a path of said beam, for defining a geometry of the beam prior to its impingement upon said specimen. The aperture region includes a distribution of multiple holes, each of which is smaller than a diameter of the beam incident on the aperture plate.

Abstract: Examining a sample in a charged-particle microscope of a scanning transmission type includes: Providing a beam of charged particles that is directed from a source through an illuminator so as to irradiate the sample; Providing a detector for detecting a flux of charged particles traversing the sample; Causing said beam to scan across a surface of the sample, and recording an output of the detector as a function of scan position, resulting in accumulation of a charged-particle image of the sample, Embodying the detector to comprise a plurality of detection segments; Combining signals from different segments of the detector so as to produce a vector output from the detector at each scan position, and compiling this data to yield a vector field; and Mathematically processing said vector field by subjecting it to a two-dimensional integration operation, thereby producing an integrated vector field image.

Abstract: A method of accumulating an image of a specimen using a scanning-type microscope, comprising the following steps: Directing a beam of radiation from a source through an illuminator so as to irradiate a surface S of the specimen; Using a detector to detect a flux of radiation emanating from the specimen in response to said irradiation; Causing said beam to follow a scan path relative to said surface; For each of a set of sample points in said scan path, recording an output Dn of the detector as a function of a value Pn of a selected measurement parameter P, thus compiling a measurement set M={(Dn, Pn)}, where n is a member of an integer sequence; Using computer processing apparatus to automatically deconvolve the measurement set M and spatially resolve it so as to produce reconstructed imagery of the specimen, wherein, considered at a given point pi within the specimen, the method comprises the following steps: In a first probing session, employing a first beam configuration B1 to irradiate the point pi w