US 11,380,529 B2Granted

Depth reconstruction for 3D images of samples in a charged particle system

AssigneeFEI Company

InventorsPavel Potocek, Milo{hacek over (s)} Hovorka, Maurice Peemen, Luká{hacek over (s)} Hübner

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Abstract

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.

Claims20

Dependent claims

A method for generating improved 3D reconstructions of a sample via slice and view charged particle imaging, the method comprising:

acquiring first data relating to a first layer of a sample, the first data having been acquired by a first irradiation of the first layer of the sample with a charged particle beam;
acquiring second data relating to a second layer of a sample, the second data having been acquired by a second irradiation of the second layer of the sample with the charged particle beam, wherein:
the first layer of the sample is removed between the first irradiation and the second irradiation;
and
an electron interaction depth of the charged particle beam is larger than the thickness of the first layer and the thickness of the second layer;
enhancing the first data with a training based 3D blind deconvolution algorithm to create enhanced first data, wherein the training based 3D blind deconvolution algorithm reduces depth blur caused by first portions of the first data resultant from electron interaction outside the first layer;
enhancing the second data with the training based 3D blind deconvolution algorithm to create enhanced second data, wherein the training based 3D blind deconvolution algorithm reduces depth blur caused by second portions of the second data resultant from electron interaction outside the second layer;
and
constructing a high-resolution 3D reconstruction of the sample using the enhanced first data and the enhanced second data.

6 more elements

A charged particle microscope system, the system comprising:

a sample holder configured to hold a sample;
an electron beam source configured to emit an electron beam toward the sample;
an electron beam column configured to direct the electron beam onto the sample;
a delayering component configured to remove a layer from the surface of the sample, the layer having a known thickness;
one or more detectors configured to detect emissions resultant from the electron beam irradiating the sample;
one or more processors;
and
a memory storing instructions that when executed on the one or more processors causes the charged particle microscope system to:
acquire first data relating to a first layer of the sample, the first data having been acquired by a first irradiation of the first layer of the sample with the electron beam;
acquire second data relating to a second layer of a sample, the second data having been acquired by a second irradiation of the second layer of the sample with the electron beam, wherein:
the first layer of the sample is removed by the delayering component between the first irradiation and the second irradiation;
and
an electron interaction depth of the electron beam is larger than the thickness of the first layer and the thickness of the second layer;
enhance the first data with a training based 3D blind deconvolution algorithm to create enhanced first data, wherein the training based 3D blind deconvolution algorithm reduces depth blur caused by first portions of the first data resultant from electron interaction outside the first layer;
enhance the second data with the training based 3D blind deconvolution algorithm to create enhanced second data, wherein the training based 3D blind deconvolution algorithm reduces depth blur caused by second portions of the second data resultant from electron interaction outside the second layer;
and
construct a high resolution 3D reconstruction of the sample using the enhanced first data and the enhanced second data.

14 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/039,513Filed 30 Sep 2020
Published: US 2022/0102121 A131 Mar 2022
Granted: 5 Jul 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

H01J 37/222
Optical , image processing or photographic arrangements associated with the tube: image processing arrangements associated with the tube
H01J 37/32926
Plasma diagnostics: software, data control or modelling
H01J 37/28
Electron or ion microscopes; Electron or ion diffraction tubes: with scanning beams
H01J 2237/2004
Environmental cells: biological samples