Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A method, a system, and a non-transitory computer readable medium for evaluating x-ray signals. The method may include calculating an estimated field for each of multiple non-perturbed objects, the multiple non-perturbed objects represent perturbances of the perturbed object; the perturbances are of an order of a wavelength of the non-diffused x-ray signals; and evaluating the non-diffused x-ray signals based on the field of the multiple non-perturbed objects.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

Abstract: A metrology method for use in determining one or more parameters of a patterned structure, the method including providing raw measured TEM image data, TEMmeas, data indicative of a TEM measurement mode, and predetermined simulated TEM image data including data indicative of one or more simulated TEM images of a structure similar to the patterned structure under measurements and a simulated weight map including weights assigned to different regions in the simulated TEM image corresponding to different features of the patterned structure, performing a fitting procedure between the raw measured TEM image data and the predetermined simulated TEM image data and determining one or more parameters of the structure from the simulated TEM image data corresponding to a best fit condition.

Abstract: The present invention may include acquiring a plurality of overlay metrology measurement signals from a plurality of metrology targets distributed across one or more fields of a wafer of a lot of wafers, determining a plurality of overlay estimates for each of the plurality of overlay metrology measurement signals using a plurality of overlay algorithms, generating a plurality of overlay estimate distributions, and generating a first plurality of quality metrics utilizing the generated plurality of overlay estimate distributions, wherein each quality metric corresponds with one overlay estimate distribution of the generated plurality of overlay estimate distributions, each quality metric a function of a width of a corresponding generated overlay estimate distribution, each quality metric further being a function of asymmetry present in an overlay metrology measurement signal from an associated metrology target.

Abstract: A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

Abstract: A metrology method for use in determining one or more parameters of a patterned structure, the method including providing raw measured TEM image data, TEMmeas, data indicative of a TEM measurement mode, and predetermined simulated TEM image data including data indicative of one or more simulated TEM images of a structure similar to the patterned structure under measurements and a simulated weight map including weights assigned to different regions in the simulated TEM image corresponding to different features of the patterned structure, performing a fitting procedure between the raw measured TEM image data and the predetermined simulated TEM image data and determining one or more parameters of the structure from the simulated TEM image data corresponding to a best fit condition.

Abstract: The present invention may include acquiring a plurality of overlay metrology measurement signals from a plurality of metrology targets distributed across one or more fields of a wafer of a lot of wafers, determining a plurality of overlay estimates for each of the plurality of overlay metrology measurement signals using a plurality of overlay algorithms, generating a plurality of overlay estimate distributions, and generating a first plurality of quality metrics utilizing the generated plurality of overlay estimate distributions, wherein each quality metric corresponds with one overlay estimate distribution of the generated plurality of overlay estimate distributions, each quality metric a function of a width of a corresponding generated overlay estimate distribution, each quality metric further being a function of asymmetry present in an overlay metrology measurement signal from an associated metrology target.

Abstract: A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

Abstract: An overlay metrology system includes one or more processors coupled to an illumination source to direct illumination to a sample and a detector to capture diffracted orders of radiation from the sample. The system may generate overlay sensitivity calibration parameters based on differential measurements of a calibration target including two overlay target cells on the sample, where first-layer target elements and second-layer target elements of the overlay target cells are distributed with a common pitch along a measurement direction and are misregistered with a selected offset value in opposite directions. The system may further determine overlay measurements based on differential measurements of additional overlay target cells with two wavelengths, where first-layer target elements and second-layer target elements of the additional overlay target cells are distributed with the common pitch and are formed to overlap symmetrically.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A control system is presented for use in measuring one or more parameters of a three-dimensional patterned structure. The control system is configured as a computer system comprising a data processor configured to receive and process raw measured TEM image data, TEMmeas, and generate output data indicative of one or more parameters of a patterned structure. The data processor comprises an optimization module configured and operable to utilize data indicative of one or more parameters of TEM measurement mode and perform a fitting procedure between the raw measured TEM image data, TEMmeas, and a predetermined simulated TEM image data, TEMsimui based on a parametrized three-dimensional model of features of the patterned structure, and generate simulated image data corresponding to a best fit condition, to thereby enable determination therefrom of the one or more parameters of the structure.

Abstract: Methods are provided for deriving a partially continuous dependency of metrology metric(s) on recipe parameter(s), analyzing the derived dependency, determining a metrology recipe according to the analysis, and conducting metrology measurement(s) according to the determined recipe. The dependency may be analyzed in form of a landscape such as a sensitivity landscape in which regions of low sensitivity and/or points or contours of low or zero inaccuracy are detected, analytically, numerically or experimentally, and used to configure parameters of measurement, hardware and targets to achieve high measurement accuracy. Process variation is analyzed in terms of its effects on the sensitivity landscape, and these effects are used to characterize the process variation further, to optimize the measurements and make the metrology both more robust to inaccuracy sources and more flexible with respect to different targets on the wafer and available measurement conditions.

Abstract: A control system for use in measuring one or more parameters of a patterned structure. The control system is configured as a computer system and comprises: an input utility configured to receive input data comprising raw measured TEM image data, TEMmeas, data indicative of a TEM measurement mode; and a data processor configured to process the raw measured TEM image data, TEMmeas, and generate output data indicative of one or more parameters of a patterned structure. The data processor comprises an optimization module configured and operable to utilize the data indicative of the TEM measurement mode and perform a fitting procedure between the raw measured TEM image data, TEMmeas, and predetermined simulated TEM image data, TEMsimul, and determine one or more parameters of the structure from the simulated image data corresponding to a best fit condition.

Abstract: Metrology methods are provided, which comprise identifying overlay critical patterns in a device design, the overlay critical patterns having an overlay sensitivity to process variation above a specified threshold that depends on design specifications; and using metrology targets that correspond to the identified overlay critical patterns. Alternatively or complementarily, metrology methods comprise identifying yield critical patterns according to a corresponding process window narrowing due to specified process variation, wherein the narrowing is defined by a dependency of edge placement errors (EPEs) of the patterns on process parameters. Corresponding targets and measurements are provided.

Abstract: Systems and methods are provided for sharing a device identifier between two applications installed on an unmanaged device. An enterprise application running on a device may execute client-side code received from an ID matching server to generate a target data set characterizing the device. The enterprise application may send the target data set to the ID matching server. The ID matching server may interact with a Mobile Threat Defense (MTD) server to determine a device ID that the MTD server may use to identify the device. The ID matching server may send the device identifier to an Identity Management (IdM) server. The IdM server may send an API request for security information about the target device to the MTD server, which may send the requested security information in response. The IdM server may determine an authorization level based on the security information.