Methods for using electron diffraction holography to investigate a sample, according to the present disclosure include the initial steps of emitting a plurality of electrons toward the sample, forming the plurality of electrons into a first electron beam and a second electron beam, and modifying the focal properties of at least one of the two beams such that the two beams have different focal planes. Once the two beams have different focal planes, the methods include focusing the first electron beam such that it has a focal plane at or near the sample, and focusing the second electron beam so that it is incident on the sample, and has a focal plane in the diffraction plane. An interference pattern of the first electron beam and the diffracted second electron beam is then detected in the diffraction plane, and then used to generate a diffraction holograph.

Atomic position data may be obtained from x-ray diffraction data. The x-ray diffraction data for a sample may be squared and/or otherwise operated on to obtain input data for a neural network. The input data may be input to a trained convolutional neural network. The convolutional neural network may have been trained based on pairs of known atomic structures and corresponding neural network inputs. For the neural network input corresponding to the sample and input to the trained convolutional neural network, the convolutional neural network may obtain an atomic structure corresponding to the sample.

The present invention refers to a method for improving a Transmission Kikuchi Diffraction, TKD pattern, wherein the method comprises the steps of: Detecting a TKD pattern (20b) of a sample (12) in an electron microscope (60) comprising at least one active electron lens (61) focussing an electron beam (80) in z-direction on a sample (12) positioned in distance D below the electron lens (61), the detected TKD (20b) pattern comprising a plurality of image points x.sub.D, y.sub.D and mapping each of the detected image points x.sub.D, y.sub.D to an image point of an improved TKD pattern (20a) with the coordinates x.sub.0, y.sub.0 by using and inverting generalized terms of the form x.sub.D=γ*A+(1−γ)*B and y.sub.D=γ*C+(1−γ)*D wherein

[00001]$\gamma =\frac{Z}{D}$ with Z being an extension in the z-direction of a cylindrically symmetric magnetic field B.sub.Z of the electron lens (61), and wherein A, B, C, D are trigonometric expressions depending on the coordinates x.sub.0, y.sub.0, with B and D defining a rotation around a symmetry axis of the magnetic field B.sub.Z, and with A and C defining a combined rotation and contraction operation with respect to the symmetry axis of the magnetic field B.sub.Z. The invention further relates to a measurement system, computer program and computer-readable medium for carrying out the method of the invention.

A method for controlling the crystallographic orientation of at least one grain of a turbo engine part. The method includes emitting a beam of electromagnetic radiation through an elementary volume of the part and record diffraction information on the electromagnetic radiation passing through the part. This step is repeated on a given area of the part. The method further includes determining the crystal spatial orientation of each of said elementary volumes and deducing the presence of at least one first crystallographic grain for which the elementary volumes are oriented according to the same crystallographic orientation. The method further includes calculating the angular difference between the crystal spatial orientation of said first grain and a predetermined direction taken from the part and comparing it to a first predetermined threshold value and determining a state of use of the part.

A charged particle beam device includes: a plurality of detecting units which detect charged particles diffracted by a specimen; and an intensity pattern information generating unit which generates, based on intensities of a plurality of detection signals output from the plurality of detecting units, intensity pattern information that represents the intensities of the plurality of detection signals as a pattern.

A measured pattern acquisition unit acquires a measured X-ray scattering pattern of a sample containing a target substance and another known mixed substance. A known pattern acquisition unit acquires a known X-ray scattering pattern of the other known mixed substance. A crystalline pattern acquisition unit at least partially acquires an X-ray diffraction pattern of a crystalline portion included in the target substance. A crystalline integrated intensity calculation unit calculates an integrated intensity for the acquired X-ray diffraction pattern of the crystalline portion. A target substance integrated intensity calculation unit calculates an integrated intensity for an X-ray scattering pattern of the target substance. A degree-of-crystallinity calculation unit calculates a degree of crystallinity of the target substance based on the integrated intensity for the X-ray diffraction pattern of the crystalline portion and the integrated intensity for the X-ray scattering pattern of the target substance.

An X-ray diffraction method measures crystallite size distribution in a polycrystalline sample using an X-ray diffractometer with a two-dimensional detector. The diffraction pattern collected contains several spotty diffraction rings. The spottiness of the diffraction rings is related to the size, size distribution and orientation distribution of the crystallites as well as the diffractometer condition. The invention allows obtaining of the diffraction intensities of all measured crystallites at perfect Bragg condition so that the crystallite size distribution can be measured based on the 2D diffraction patterns.

Atomic position data may be obtained from x-ray diffraction data. The x-ray diffraction data for a sample may be squared and/or otherwise operated on to obtain input data for a neural network. The input data may be input to a trained convolutional neural network. The convolutional neural network may have been trained based on pairs of known atomic structures and corresponding neural network inputs. For the neural network input corresponding to the sample and input to the trained convolutional neural network, the convolutional neural network may obtain an atomic structure corresponding to the sample.

A quantitative phase analysis device for analyzing non-crystalline phases comprising at least one microprocessor configured to: acquire the powder diffraction pattern of the sample; acquire information on one non-crystalline phase and one or more crystalline phases contained in the sample; acquire a fitting function; execute whole-powder pattern fitting, acquire a fitting result; and calculate a weight ratio of the one non-crystalline phase and the one or more crystalline phases. The fitting function for each of the one or more crystalline phases is one fitting function selected from the group consisting of a first fitting function that uses an integrated intensity obtained by whole-powder pattern decomposition, a second fitting function that uses an integrated intensity obtained by observation or calculation, and a third fitting function that uses a profile intensity obtained by observation or calculation. The fitting function for the one non-crystalline phase is the third fitting function.

A method of processing X-ray diffraction data, the data is provided by an X-ray detector configured to detect diffracted X-ray beams of a sample. The method including acquiring X-ray diffraction data from the X-ray detector while the sample is rotating with respect to an incident X-ray beam, generating a 2D image frame from the acquired X-ray diffraction data, wherein the generated 2D image frame includes 2D image data representing X-ray diffraction data for a specific rotational position of the sample, for the generated 2D image frame, distinguishing the sample relevant X-ray diffraction data from the background data; mapping the distinguished sample relevant X-ray diffraction data of the generated 2D image frame into a single 3D reciprocal space; and visualizing the 3D reciprocal space along with the mapped X-ray diffraction data on a display screen. Further provided is an apparatus and an X-ray device implementing the method.