# US 12,223,752 B2 · Data acquisition in charged particle microscopy

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- Number: US 12,223,752 B2
- Document ID: US12223752B2
- Status: Granted
- Kind: B2
- Application number: 17/491,260
- Filed: 2021-09-30
- Published: 2023-03-30
- Granted: 2025-02-11
- Earliest claimed: 2021-09-30
- Assignees: FEI Company
- Applicants: FEI Company
- Inventors: Ondrej Machek; Pavel Potocek; Tereza Konečná
- Web page: https://patentgrep.com/patent/US12223752B2

## CPC

- G06T 1/0007: {Image acquisition}
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06N 3/08: Learning methods
- G06T 3/4053: based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
- G06V 10/235: {based on user input or interaction}
- G06V 20/693: {Acquisition}
- G01N 23/04: and forming images of the material
- G06T 2207/10061: from scanning electron microscope
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- H01J 2237/221: Image processing
- H01J 2237/2802: Transmission microscopes
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- H01J: ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS (spark-gaps H01T; electric arc lamps H05B31/00; particle accelerators H05H)

## Abstract

Disclosed herein are charged particle microscopy (CPM) support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a CPM support apparatus may include: first logic to cause a CPM to generate a single image of a first portion of a specimen; second logic to generate a first mask based on one or more regions-of-interest provided by user annotation of the single image; and third logic to train a machine-learning model using the single image and the one or more regions-of-interest. The first logic may cause the CPM to generate multiple images of corresponding multiple additional portions of the specimen, and the second logic may, after the machine-learning model is trained using the single image and the one or more regions-of-interest, generate multiple masks based on the corresponding images of the additional portions of the specimen using the machine-learning model without retraining.

## Claims

### Claim 1

A charged particle microscope support apparatus, comprising:
first logic to cause a charged particle microscope to generate a single image of a first portion of a specimen;
second logic to generate a first mask based on one or more regions-of-interest provided by user annotation of the single image; and
third logic to train a machine-learning computational model using the single image and the one or more regions-of-interest;
wherein:
the first logic is to cause the charged particle microscope to generate a plurality of images of a corresponding plurality of additional portions of the specimen; and
the second logic is to, after the machine-learning computational model is trained using the single image and the one or more regions-of-interest, generate a plurality of masks based on the corresponding images of the additional portions of the specimen using the machine-learning computational model without retraining.

### Claim 2 (depends on 1)

The charged particle microscope support apparatus of claim 1, wherein the first logic is to, after generation of the first mask, cause the charged particle microscope to generate an other image of the first portion of the specimen in accordance with the first mask.

### Claim 3 (depends on 2)

The charged particle microscope support apparatus of claim 2, wherein a resolution of the single image of the first portion of the specimen is less than a resolution of the other image of the first portion of the specimen.

### Claim 4 (depends on 2)

The charged particle microscope support apparatus of claim 2, wherein an acquisition time of the single image of the first portion is less than an acquisition time of the other image of the first portion of the specimen.

### Claim 5 (depends on 2)

The charged particle microscope support apparatus of claim 2, further comprising:
fourth logic to combine the single image and the other image into a combined image representative of the first portion of the specimen, and to provide the combined image for display by a display device.

### Claim 6 (depends on 1)

The charged particle microscope support apparatus of claim 1, wherein 1) the first portion of the specimen represents a plane through the specimen, and the plurality of additional portions of the specimen represent a plurality of parallel planes through the specimen, or 2) the first portion of the specimen represents a plane through the specimen, and the plurality of additional portions of the specimen represent a plurality of planes through the specimen at different angles.

### Claim 7 (depends on 1)

The charged particle microscope support apparatus of claim 1, wherein the second logic is to:
provide the single image of the first portion of the specimen for display on a display device; and
receive, from a user, an indication of one or more regions-of-interest by user annotations of the displayed single image.

### Claim 8 (depends on 1)

The charged particle microscope support apparatus of claim 1, wherein the second logic is to compare the masks associated with adjacent portions of the specimen and, when differences between the masks meet one or more difference criteria, adjust one or more of the masks.

### Claim 9

A charged particle microscope support apparatus, comprising:
first logic to cause a charged particle microscope to generate a single image of a first portion of a specimen;
second logic to generate a first mask based on one or more regions-of-interest indicated by user annotation of the single image, wherein the regions-of-interest include a feature-of-interest in the specimen; and
third logic to train a machine-learning computational model using the single image and the one or more regions-of-interest;
wherein:
the first logic is to cause the charged particle microscope to generate an image of a second portion of the specimen, wherein the second portion of the specimen is proximate to the first portion of the specimen; and
the second logic is to, after the machine-learning computational model is trained using the single image and the one or more regions-of-interest, generate a second mask based on the image of the second portion of the specimen using the machine-learning computational model, wherein the second mask indicates to image regions of the second portion of the specimen including the feature-of-interest and regions of the second portion of the specimen that do not include the feature-of-interest.

### Claim 10 (depends on 9)

The charged particle microscope support apparatus of claim 9, further comprising:
fourth logic to generate a three-dimensional image of the specimen using images captured in accordance with the masks.

### Claim 11 (depends on 9)

The charged particle microscope support apparatus of claim 9, wherein the first portion of the specimen is spaced apart from the second portion of the specimen by a distance between 1 micron and 30 microns.

### Claim 12 (depends on 11)

The charged particle microscope support apparatus of claim 11, wherein the first portion of the specimen and the second portion of the specimen are spaced apart in the (z)-direction.

### Claim 13 (depends on 9)

The charged particle microscope support apparatus of claim 9, wherein, before the machine-learning computational model is trained using the single image and the one or more regions-of-interest, the machine-learning computational model is trained using a training corpus that does not include images of the specimen.

### Claim 14 (depends on 13)

The charged particle microscope support apparatus of claim 13, wherein the training corpus does not include any examples of the feature-of-interest.

### Claim 15 (depends on 9)

The charged particle microscope support apparatus of claim 9, wherein the second logic is to compare the first and second masks and, when differences between the masks meet one or more difference criteria, adjust the second mask.