An analysis method includes: obtaining n×m pieces of map data by repeating, m times, a map measurement in which n pieces of map data are obtained by scanning a specimen with a primary probe to detect electrons emitted from the specimen with an electron spectrometer, while measurement energy ranges of an analyzer are varied; and generating a spectral map in which a position on the specimen is associated with a spectrum based on the n×m pieces of map data, the measurement energy ranges of m times of the map measurement not overlapping each other.

Molecular structure of a crystal may be solved based on at least two diffraction tilt series acquired from a sample. The two diffraction tilt series include multiple diffraction patterns of at least one crystal of the sample acquired at different electron doses. In some examples, the two diffraction tilt series are acquired at different magnifications.

Crystallographic information of crystalline sample can be determined from one or more three-dimensional diffraction pattern datasets generated based on diffraction patterns collected from multiple crystals. The crystals for diffraction pattern acquisition may be selected based on a sample image. At a location of each selected crystal, multiple diffraction patterns of the crystal are acquired at different angles of incidence by tilting the electron beam, wherein the sample is not rotated while the electron beam is directed at the selected crystal.

Provided is a method for virtually executing an operation of an energy dispersive x-ray spectrometry (EDS) system in real time production line by analyzing a defect included in a material undergoing inspection based on computer vision, the method including receiving a scanning electron microscope (SEM) image of the material including the defect, extracting an image-feature from the SEM image of the material, classifying the extracted image-feature under a predetermined label, predicting, based on the classified image-feature, an element associated with the defect included in the material and a shape of the predicted element, and grading the defect included in the material based on comparing the predicted element with a predetermined criteria.

A method for correction of thermal defects using tomographic scanning for additive manufacturing is provided. The method may include forming a portion of an object using an additive manufacturing system based on an intended three-dimensional (3D) model of the object that is in an additive manufacturing system format. The portion of the object is scanned using a tomographic scanner to obtain a model of the portion of the object in a tomographic scanner format. The model is converted from the tomographic scanner format into the additive manufacturing system format to obtain a converted tomographic model; and the converted tomographic model is compared to the intended 3D model to identify a defect in the portion of the object. A modified 3D model may be generated of the object correcting the intended 3D model to address the defect of the portion of the object.

Provided is a method for virtually executing an operation of an energy dispersive x-ray spectrometry (EDS) system in real time production line by analyzing a defect included in a material undergoing inspection based on computer vision, the method including receiving a scanning electron microscope (SEM) image of the material including the defect, extracting an image-feature from the SEM image of the material; classifying the extracted image-feature under a predetermined label, predicting, based on the classified image-feature, an element associated with the defect included in the material and a shape of the predicted element, and grading the defect included in the material based on comparing the predicted element with a predetermined criteria.

Crystallographic information of crystalline sample can be determined from one or more three-dimensional diffraction pattern datasets generated based on diffraction patterns collected from multiple crystals. The crystals for diffraction pattern acquisition may be selected based on a sample image. At a location of each selected crystal, multiple diffraction patterns of the crystal are acquired at different angles of incidence by tilting the electron beam, wherein the sample is not rotated while the electron beam is directed at the selected crystal.

A multi-channel static CT device is provided, and the multi-channel static CT device includes: a scanning channel including a plurality of scanning sub-channels; a distributed X-ray source including a plurality of ray emission points arranged around the scanning channel; and a detector module including a plurality of detectors arranged around the scanning channel, wherein the plurality of detectors are arranged corresponding to the plurality of ray emission points.

An analysis method includes: obtaining nm pieces of map data by repeating, m times, a map measurement in which n pieces of map data are obtained by scanning a specimen with a primary probe to detect electrons emitted from the specimen with an electron spectrometer, while measurement energy ranges of an analyzer are varied; and generating a spectral map in which a position on the specimen is associated with a spectrum based on the nm pieces of map data, the measurement energy ranges of m times of the map measurement not overlapping each other.

In embodiments, the present invention describes the use of three-dimensional (3D) stationary gantry X-ray computed tomography systems to scan animals/livestock for enabling improved management of animal farming processes, functions or events. The present invention also discloses the use of 3D stationary gantry X-ray computed tomography systems for carcass screening and improved abattoir production planning, execution, and automation. In various embodiments, use of the scanning technology supports high throughput, automated, meat-processing lines with reduced manual labor, objectively measured product quality and improved food safety standards. In embodiments, the present specification discloses the use of 3D X-ray inspection to generate an image of an entire carcass and sections of the carcass, during the stages of dissection, final product preparation, and packaging of the carcass.