Fei Comapny

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Koninklijke Philips Corporation N.v.

Faysal Boughorbel

Pavel Potocek

Berend Helmerus Lich

Eric Gerardus Theodoor Bosch

Cornelis Sander Kooijman

Remco Schoenmakers

Bart Buijsse

Mathijs Petrus Wilhelmus Van Den Boogaard

Emine Korkmaz

Xiaodong Zhuge

Peter Christiaan Tiemeijer

Maurice Peemen

Gerard Nicolaas Anne Van Veen

Hendrik Nicolaas Slingerland

Ivan Lazic

Alan Frank De Jong

Sorin Lazar

Matthias Langhorst

Kasim Sader

Daniel Woodrow Phifer, Jr

Ingo Gestmann

Maurice Peeman

Franciscus Martinus Henricus Maria Van Laarhoven

Eric Bosch

Ben Lich

Cees Kooijman

Van Gerard Veen

Frank Van Laarhoven

Daniel Phifer

Frank De Jong

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.

Method, device and system for remote deep learning for microscopic image reconstruction and segmentation

Producing and storing a first image, of a first, initial surface of the specimen;

Method of analyzing surface modification of a specimen in a charged-particle microscope

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.

X-ray imaging technique

A method of investigating a specimen using charged-particle microscopy, and a charged particle microscope configured for same. In one embodiment, the method includes: (a) selecting a virtual sampling grid on a surface of a specimen, the virtual sampling grid extending in an XY plane and comprising nodes to be impinged upon by a beam of charged particles; (b) selecting a landing energy for the beam, the landing energy associated with a penetration depth; (c) generating a scan image by irradiating the specimen at each of the nodes with the beam, and detecting output radiation emanating from the specimen in response thereto; (d) repeating steps (b) and (c) for a series of different landing energies corresponding to an associated series of penetration depths, (e) pre-selecting an energy increment by which the landing energy is to be altered after a first iteration of steps (b) and (c); (f) associating the energy increment with a corresponding depth increment; (g) selecting the virtual sampling grid to have a substantially equal node pitch p in X and Y, which pitch p is matched to the value of the depth increment so as to produce a substantially cubic sampling voxel; and (h) selecting subsequent energy values in the series of landing energies so as to maintain a substantially constant depth increment between consecutive members of the series of penetration depths.

Three-dimensional imaging in charged-particle microscopy

A method of investigating a specimen using a charged particle microscope, including:

A Charged Particle Microscope includes The illuminator includes: 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.

Charged particle microscope with special aperture plate

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 Oof said detector arrangement as a function of kinetic energy Eof said electrons, thus compiling a measurement set M={(O, E)} for a plurality of values of E; using computer processing apparatus to automatically deconvolve the measurement set M and spatially resolve it into a result set R={(V, L)}, in which a spatial variable V demonstrates a value Vat an associated discrete depth level Lreferenced to the surface S, whereby n and k are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk.

Charged-particle microscope providing depth-resolved imagery

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 {S} of a set {S}, gathering data from a first collection of sparsely distributed sampling points {P} of set {P}. A mathematical registration correction is made to compensate for drift mismatches between different members of the set {P}, and an image of the specimen is assembled using the set {P} as input to an integrative mathematical reconstruction procedure.

Mathematical image assembly in a scanning-type microscope

Computational scanning microscopy with improved resolution

Examining a sample in a charged-particle microscope of a scanning transmission type includes:

Method of examining a sample in a charged-particle microscope

Charged particle microscope providing depth-resolved imagery

This invention relates to a method of examining a sample using a charged-particle microscope. This invention solves the problem of occlusion effects, whereby a given line-of-sight behind a particular region on a sample and a given detector is blocked by a topographical feature on the sample, thus hampering detection of the emitted radiation emanating from the occluded region. This problem is solved by using at least a first and second detector configuration to detect each portion of the emitted radiation and to produce at least a first and second corresponding image based thereupon; and using computer processing apparatus to automatically compare different members of the set of corresponding images and mathematically identify on the sample at least one occlusion region with an occluded line-of-sight relative to at least one of the detector configurations.

Charged-particle microscopy with occlusion detection

A method of charged-particle microscopy, comprising: irradiating a sample surface S to cause radiation to emanate from the sample; detecting at least a portion of said emitted radiation recording an output Oof said detector arrangement as a function of emergence angle &#x3b8;of said emitted radiation, measured relative to an axis normal to S thus compiling a measurement set M={(O, &#x3b8;)} for a plurality of values of &#x3b8;; automatically deconvolving the measurement set M and spatially resolve it into a result set R={(V, L)},in which a spatial variable V demonstrates a value Vat an associated discrete death level Lreferenced to the surface S, whereby n and K are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk.

A charged-particle microscopy includes irradiating a sample in measurement sessions, each having an associated beam parameter (P) value detecting radiation emitted during each measurement session, associating a measurand (M) with each measurement session, thus providing a data set (S) of data pairs {P, M}, wherein an integer in the range of 1&#x2266;n&#x2266;N, and processing the set (S) by: defining a Point Spread Function (K) having a kernel value Kfor each value n; defining a spatial variable (V); defining an imaging quantity (Q) having fore each value of n a value Qthat is a three-dimensional convolution of Kand V, such that Q=K*V; for each value of n, determining a minimum divergence min D(M&#x2225;K*V) between Mand Q, solving V while applying constraints on the values K.

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Eindhoven, Netherlands

Faysal Boughorbel