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 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.

An example scan procedure generation system includes: a display; a processor; and a computer readable storage medium comprising computer readable instructions which, when executed, cause the processor to: output, via the display, a first visual representation of an arrangement of a radiation source, a radiation detector, a workpiece positioner, and a workpiece; and based on positions and orientations of the radiation source, the radiation detector, the workpiece positioner, and the workpiece, generate a scanning procedure for execution by a physical scanner having a physical radiation source, a physical radiation detector, and a physical workpiece positioner, wherein the generated scanning procedure comprises a plurality of movements of one or more of the physical radiation source, the physical radiation detector, and the physical workpiece positioner and a plurality of image captures to capture a plurality of scan images of a physical workpiece corresponding to the workpiece in the first virtual representation.

Methods and systems for improving a measurement recipe describing a sequence of measurements employed to characterize semiconductor structures are described herein. A measurement recipe is repeatedly updated before a queue of measurements defined by the previous measurement recipe is fully executed. In some examples, an improved measurement recipe identifies a minimum set of measurement options that increases wafer throughput while meeting measurement uncertainty requirements. In some examples, measurement recipe optimization is controlled to trade off measurement robustness and measurement time. This enables flexibility in the case of outliers and process excursions. In some examples, measurement recipe optimization is controlled to minimize any combination of measurement uncertainty, measurement time, move time, and target dose. In some examples, a measurement recipe is updated while measurement data is being collected. In some examples, a measurement recipe is updated at a site while data is collected at another site.

Provided are a quantitative analysis method, a quantitative analysis program, and an X-ray fluorescence spectrometer. The quantitative analysis method includes: a step of acquiring a representative composition set to represent contents of analysis components; a step of acquiring a plurality of comparative compositions, in each of which the content of one of the analysis components of the representative composition is changed by a predetermined content; a detection intensity calculation step of calculating a detection intensity indicating an intensity of fluorescent X-rays detected under the influence of the geometry effect through use of an FP method with respect to a virtual sample having a thickness set in advance and being indicated by each of the representative composition and the comparative compositions; and a step of calculating a matrix correction coefficient for each of the analysis components based on the detection intensity.

In an inspection device having a storage unit and an exposure dose calculation unit, the exposure dose calculation unit executes a first step for calculating the dose when an image is acquired by irradiating radiation from a radiation generator based on the reference dose stored in the storage unit, a second step for calculating the dose when the relative position between the radiation generator and an inspection object is changed, a third step for calculating the total value of the dose irradiated to the inspection object, and a fourth step for outputting the total value.

A method and system are provided for model-based analysis of samples of interest and management of sample classification. Predetermined modeled data is provided including data indicative of K models for respective K measurement schemes based on a predetermined function having a spectral line shape, data indicative of M characteristic vectors of M predetermined group to which different samples relate, and data indicative of a common vector of weights for the M groups. A data processor utilizes the data and operates to apply model-based processing to measured spectral data of a sample of interest using the predetermined modeled data, and generate classification data indicative of relation of the specific sample of interest to one of the M predetermined groups.

According to an aspect of the present invention, provided is an information processing apparatus comprising a memory configured to store a program; and a processor configured to execute a program so as to output a parameter result in relation to a thin film by inputting a profile result in relation to an intensity of X-ray from the thin film to a neural network, wherein the neural network is a neural network that is allowed to machine-learn teacher data using profile data in relation to an intensity of X-ray from a thin film as input data and using parameter data in relation to the thin film as output data.

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.