Determining a property of a layer of an integrated circuit (IC), the layer being formed over an underlayer, is implemented by performing the steps of: irradiating the IC to thereby eject electrons from the IC; collecting electrons emitted from the IC and determining the kinetic energy of the emitted electrons to thereby calculate emission intensity of electrons emitted from the layer and electrons emitted from the underlayer calculating a ratio of the emission intensity of electrons emitted from the layer and electrons emitted from the underlayer; and using the ratio to determine material composition or thickness of the layer. The steps of irradiating IC and collecting electrons may be performed using x-ray photoelectron spectroscopy (XPS) or x-ray fluorescence spectroscopy (XRF).

A method for performing inspection of a manufactured article. The method comprises acquiring a sequence of radiographic images of the article; determining a position of the article for each one of the acquired radiographic images; and performing a three-dimensional model correction loop which comprises, iteratively: generating a simulated radiographic image for each determined position of the article; and comparing the simulated radiographic images and the acquired radiographic images and generating a match result. If the match result is indicative of a mismatch, the method includes identifying and characterizing differences between the simulated radiographic images and the acquired radiographic images; correcting one of a geometry and a material density of a region of interest of the detailed three-dimensional model of the article based on each one of the identified and characterized differences; and performing a new iteration. A system for performing inspection is also provided.

A metrology system is disclosed. In one embodiment, the metrology system includes a controller communicatively coupled to a reference metrology tool and an optical metrology tool, the controller including one or more processors configured to: generate a geometric model for determining a profile of a test HAR structure from metrology data from a reference metrology tool; generate a material model for determining one or more material parameters of a test HAR structure from metrology data from the optical metrology tool; form a composite model from the geometric model and the material model; measure at least one additional test HAR structure with the optical metrology tool; and determine a profile of the at least one additional test HAR structure based on the composite model and metrology data from the optical metrology tool associated with the at least one HAR test structure.

The present disclosure relates to a method for detecting a fibre misalignment in an elongated structure, such as a wind turbine blade component. The elongated structure has a length along a longitudinal direction and comprises a plurality of stacked reinforcing fibre layers. The plurality of fibre layers comprises fibres having an orientation aligned, unidirectionally, substantially in the longitudinal direction. The method comprises scanning a surface of the elongated structure for identifying one or more surface irregularities, selecting one or more regions of interest comprising said one or more surface irregularities, examining said region of interest using penetrating radiation, and determining a position and/or size of the fibre misalignment based on said examining step.

A method of analysing a subterranean drilled core sample 10 is disclosed. The steps followed are: a) providing a drill core sample 10 taken from a subterranean formation; b) producing high-resolution data of at least a section of the drill core sample 10 and creating a 3D before test skeleton of the sample 10 using that data; c) mimic wellbore operations using reservoir conditions core floods; d) producing high-resolution data of at least a section of the drill core sample 10 and creating a 3D after test skeleton of the sample using that data; e) identifying and/or segregating one or more formation damage mechanisms 12 by subtracting the 3D before test skeleton from the 3D after test skeleton to create a 3D change skeleton which shows all the formation damage mechanisms 12; and f) 1) identify one or more individual formation damage mechanisms 12, by conducting segmentation including performing one or more diagnostic analysis techniques on at least a section of the drill core sample 10 and generating individual or combinations of simulated 3D skeletons; and 2) determining the effect of said formation damage mechanism(s) 12 on a chosen characteristic of interest of said drill core sample 10.

A system (100) for producing analysis data indicative of presence of one or more predetermined components in a sample (110) is presented. The system includes source equipment (120) for directing a particle stream (130) towards the sample (110), detector equipment (140) for measuring a distribution of particles scattered from the sample (110) as a function of a scattering angle (), and processing equipment (170) for producing the analysis data based on the measured distribution of the scattered particles and on reference information indicative of an effect of the one or more predetermined components on the distribution of the scattered particles. The scattering angle related to each scattered particle is an angle between an arrival direction of the particle stream and a trajectory (160) of the scattered particle. The system utilizes different directional properties of scattering related to different isotopes, different chemical substances, and different isomers.

Determining a property of a layer of an integrated circuit (IC), the layer being formed over an underlayer, is implemented by performing the steps of: irradiating the IC to thereby eject electrons from the IC; collecting electrons emitted from the IC and determining the kinetic energy of the emitted electrons to thereby calculate emission intensity of electrons emitted from the layer and electrons emitted from the underlayer calculating a ratio of the emission intensity of electrons emitted from the layer and electrons emitted from the underlayer; and using the ratio to determine material composition or thickness of the layer. The steps of irradiating IC and collecting electrons may be performed using x-ray photoelectron spectroscopy (XPS) or x-ray fluorescence spectroscopy (XRF).

A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.

Disclosed herein is a computer-based method for non-destructive depth-profiling of samples. The method includes a measurement operation and a data analysis operation. The measurement operation includes, for each of a plurality of landing energies: (i) projecting an electron beam on a sample, which penetrates the sample to a respective depth determined by the landing energy, and (ii) sensing electrons returned from the sample, thereby obtaining a respective sensed electrons data set. The data analysis operation includes generating from the sensed electrons data sets a concentration map, which characterizing at least a vertical dimension of the sample.

Methods and systems for estimating values of parameters of interest from X-ray scatterometry measurements with reduced computational effort are described herein. Values of parameters of interest are estimated by regression using a trained, machine learning (ML) based electromagnetic (EM) response model. A training data set includes sets of Design Of Experiments (DOE) values of parameters of interest and corresponding DOE values of a plurality of electromagnetic response metrics. In some examples, values of parameters of interest are determined from measured images based on regression using a sequence of trained ML based electromagnetic response models. In some examples, input values employed to train the ML based EM response model are scaled based on model output variation.