Provided are a method, a system, and a storage medium for obtaining an energy spectrum. The method comprises: obtaining a first depth dose curve corresponding to a first energy and a second depth dose curve corresponding to a second energy; obtaining, based on the first depth dose curve and the second depth dose curve, a first set of dose values and a second set of dose values along a depth direction, respectively; determining a relationship between the first depth dose curve and the second depth dose curve based on the first set of dose values and the second set of dose values; obtaining a first energy spectrum corresponding to the first depth dose curve; and determining a second energy spectrum corresponding to the second depth dose curve based on the first energy spectrum and the relationship between the first depth dose curve and the second depth dose curve.

An X-ray inspection apparatus includes an X-ray generator; an X-ray detector; and a determination unit determining a quality state of an inspection object, based on an X-ray detection signal. The apparatus has an X-ray image storing unit storing a first inspection image, corresponding to the X-ray detection signal outputted from the X-ray detector, whose observation direction is the direction in which the X-rays transmits the inspection object; a pseudo three-dimensional information generation model generating pseudo three-dimensional information regarding a type of object to be learned; and an inspection image generation unit creating a second inspection image regarding the type of object to be learned having an observation direction different from the first inspection image, based on the first inspection image regarding the type of object to be learned. The determination unit performs the determination based on at least the second inspection image created by the inspection image generation unit.

The sample component determination method includes: acquiring a spectrum of a sample which is measured by a wavelength dispersive X-ray analyzer; defining a target element to be analyzed in the sample and an input wavelength range corresponding to the target element; and determining a chemical bonding state of the target element in the sample by inputting a partial spectrum of the sample spectrum that falls within the input wavelength range to a first trained model.

An object of the present disclosure is to obtain sufficient analysis accuracy and throughput when a sample is analyzed using two or more element analysis devices having different energy resolutions. An analysis system according to the present disclosure performs elemental analysis by using a second element analysis device having higher energy resolution than a first element analysis device, based on a result obtained by comparing a first energy spectrum of a target sample acquired by using the first element analysis device with a second energy spectrum of a reference sample acquired by using the first element analysis device (see FIG. 2).

There are provided systems and methods comprising obtaining an acquisition signal informative of a semiconductor specimen comprising at least a first layer located at a first depth and a second layer located at a second depth, wherein the acquisition signal has been acquired by an electron beam examination system operative to scan the specimen with an electron beam associated with a landing energy enabling generating, in at least one of the acquisition signal or in a signal derived from the acquisition signal, a first pattern informative of a lateral edge of the first layer, and a second pattern informative of a lateral edge of the second layer, wherein the second pattern differs from the first pattern, and using at least one of the acquisition signal or the signal derived from the acquisition signal, to determine properties of at least one of the first layer or the second layer.

Disclosed herein is a system for non-destructive characterization of specimens. The system includes an electron beam (e-beam) source for projecting e-beams at one or more e-beam landing energies on a specimen; an X-ray detector for sensing X-rays emitted from the specimen, thereby obtaining measurement data; and a processing circuitry. The processing circuitry is configured to: (i) extract from the measurement data key features specified by a vector {right arrow over ()}.sub.key; and (ii) estimate values {right arrow over (p)} of one or more structural parameters characterizing the specimen, based on {right arrow over ()}.sub.key and a set of vectors of key features {{right arrow over ()}.sub.n}.sub.n=1.sup.N of ground truth (GT) reference specimens. Each of the {right arrow over ()}.sub.n is a product of measurements of emission of X-rays from a reference specimen due to impinging thereof with e-beams at each of the one or more landing energies.

One or more X-ray intensities for a sample may be approximated. Measured intensities are received from an X-ray detection system at one or more diffraction angles. A sample simulation module computes simulated sample intensities from an X-ray fluorescence sample model with initial sample model parameters indicating the sample composition and/or layer thickness of one or more sample layers. A correction module applies a triangular collimator correction to the simulated sample intensities and determines a mathematical distance between the corrected simulated sample intensities and corresponding measured intensities. The sample model parameters are adjusted and the correction steps are repeated until the distance change falls below a minimal distance change. The sample model parameters regarding sample composition and/or the layer thickness associated with the corrected simulated intensities are provided as approximated concentration values of respective components contained in the measured sample and/or the layer thickness of the measured sample.

A training data generation device generates training data usable in machine learning. A learned model using the training data generated by the training data generation device is used in an inspection device for determining whether an inspection target is a normal product by inputting an image capturing the inspection target into the learned model. The training data generation device includes: a determination-target image extraction unit that extracts, from an input image, one or more determination-target images containing a determination target that satisfies a predetermined condition; a sorting unit that associates, on the basis of sorting the inspection target captured in the determination-target image, each of the determination-target images and a result of the sorting with each other; and a training data memory unit that stores training data in which each of the determination-target images and a result of the sorting are associated with each other.

A measurement device includes an analyzer configured to analyze a diffraction image of X-rays scattered from a subject; estimate a surface contour shape of a measurement area of the subject; extract feature data from shape information, and determine shape parameters for representing the surface contour shape; calculate a theoretical scattering intensity of each of the scattered X-rays when values of the shape parameters are changed; calculate a difference between a measured scattering intensity of each scattered X-ray and the corresponding theoretical scattering intensity, and generate a regression model of a relationship between a corresponding value of the shape parameter and the difference for each shape parameter; extract one shape parameter candidate value reducing the difference from the regression model, and calculate a theoretical scattering intensity of the shape parameter candidate value; and estimate the value of the shape parameter minimizing the difference while repeatedly changing the shape parameter candidate value.

A system to characterize a film layer within a measurement box is disclosed. The system obtains a first mixing fraction corresponding to a first X-ray beam, the mixing fraction represents a fraction of the first X-ray beam inside a measurement box of a wafer sample, the measurement box represents a bore structure disposed over a substrate and having a film layer disposed inside the bore structure. The system obtains a contribution value for the measurement box corresponding to the first X-ray beam, the contribution value representing a species signal outside the measurement box that contributes to a same species signal inside the measurement box. The system obtains a first measurement detection signal corresponding to a measurement of the measurement box using the first X-ray beam. The system determines a measurement value of the film layer based on the first measurement detection signal, the contribution value, and the first mixing fraction.