Abstract: Methods and systems for performing simultaneous X-ray Fluorescence (XRF) and small angle x-ray scattering (SAXS) measurements over a desired inspection area of a specimen are presented. SAXS measurements combined with XRF measurements enables a high throughput metrology tool with increased measurement capabilities. The high energy nature of x-ray radiation penetrates optically opaque thin films, buried structures, high aspect ratio structures, and devices including many thin film layers. SAXS measurements of a particular location of a planar specimen are performed at a number of different out of plane orientations. This increases measurement sensitivity, reduces correlations among parameters, and improves measurement accuracy. In addition, specimen parameter values are resolved with greater accuracy by fitting data sets derived from both SAXS and XRF measurements based on models that share at least one material parameter. The fitting can be performed sequentially or in parallel.

Abstract: Methods and systems for characterizing dimensions and material properties of high aspect ratio, vertically manufactured devices using transmission, small-angle x-ray scattering (T-SAXS) techniques are described herein. Exemplary structures include spin transfer torque random access memory (STT-RAM), vertical NAND memory (V-NAND), dynamic random access memory (DRAM), three dimensional FLASH memory (3D-FLASH), resistive random access memory (Re-RAM), and PC-RAM. In one aspect, T-SAXS measurements are performed at a number of different orientations that are more densely concentrated near the normal incidence angle and less densely concentrated at orientations that are further from the normal incidence angle. In a further aspect, T-SAXS measurement data is used to generate an image of a measured structure based on the measured intensities of the detected diffraction orders. In another further aspect, a metrology system is configured to generate models for combined x-ray and optical measurement analysis.

Abstract: Methods and systems for evaluating the performance of multiple patterning processes are presented. Patterned structures are measured and one or more parameter values characterizing geometric errors induced by the multiple patterning process are determined. In some examples, a single patterned target and a multiple patterned target are measured, the collected data fit to a combined measurement model, and the value of a structural parameter indicative of a geometric error induced by the multiple patterning process is determined based on the fit. In some other examples, light having a diffraction order different from zero is collected and analyzed to determine the value of a structural parameter that is indicative of a geometric error induced by a multiple patterning process. In some embodiments, a single diffraction order different from zero is collected. In some examples, a metrology target is designed to enhance light diffracted at an order different from zero.

Abstract: Structural parameters of a specimen are determined by fitting models of the response of the specimen to measurements collected by different measurement techniques in a combined analysis. X-ray measurement data of a specimen is analyzed to determine at least one specimen parameter value that is treated as a constant in a combined analysis of both optical measurements and x-ray measurements of the specimen. For example, a particular structural property or a particular material property, such as an elemental composition of the specimen, is determined based on x-ray measurement data. The parameter(s) determined from the x-ray measurement data are treated as constants in a subsequent, combined analysis of both optical measurements and x-ray measurements of the specimen. In a further aspect, the structure of the response models is altered based on the quality of the fit between the models and the corresponding measurement data.

Abstract: Methods and systems for evaluating the performance of multiple patterning processes are presented. Patterned structures are measured and one or more parameter values characterizing geometric errors induced by the multiple patterning process are determined. In some examples, a single patterned target and a multiple patterned target are measured, the collected data fit to a combined measurement model, and the value of a structural parameter indicative of a geometric error induced by the multiple patterning process is determined based on the fit. In some other examples, light having a diffraction order different from zero is collected and analyzed to determine the value of a structural parameter that is indicative of a geometric error induced by a multiple patterning process. In some embodiments, a single diffraction order different from zero is collected. In some examples, a metrology target is designed to enhance light diffracted at an order different from zero.

Abstract: Provided are scatterometry model optimizations for evaluating a diffracting structure. In one embodiment, a method includes identifying one or more parameters to test for inclusion in the model. The method includes computing a difference between modeled data generated with the one or more parameters and measured data. The one or more parameters are included in the model in response to a reduction in the difference between the modeled data and the measured data compared to previous modeled, and passing a statistical hypothesis test. In one embodiment, the one or more parameters pass the statistical hypothesis test in response to the probability of obtaining the reduction in the difference is less than a significance level. In one embodiment, the significance level is a function of an alpha wealth value. In one embodiment, the method includes ordering the plurality of parameters for testing according to one or more criteria.

Abstract: A system, method and computer program product are provided for combining raw data from multiple metrology tools. Reference values are obtained for at least one parameter of a training component. Signals are collected for the at least one parameter of the training component, utilizing a first metrology tool and a different second metrology tool. Further, at least a portion the signals are transformed into a set of signals, and for each of the at least one parameter of the training component, a corresponding relationship between the set of signals and the reference values is determined and a corresponding training model is created therefrom. Signals from a target component are collected utilizing at least the first metrology tool and the second metrology tool, and each created training model is applied to the signals collected from the target component to measure parametric values for the target component.

Abstract: Methods and systems for evaluating the capability of a measurement system to track measurement parameters through a given process window are presented herein. Performance evaluations include random perturbations, systematic perturbations, or both to effectively characterize the impact of model errors, metrology system imperfections, and calibration errors, among others. In some examples, metrology target parameters are predetermined as part of a Design of Experiments (DOE). Estimated values of the metrology target parameters are compared to the known DOE parameter values to determine the tracking capability of the particular measurement. In some examples, the measurement model is parameterized by principal components to reduce the number of degrees of freedom of the measurement model. In addition, exemplary methods and systems for optimizing the measurement capability of semiconductor metrology systems for metrology applications subject to process variations are presented.

Abstract: Methods and systems for estimating values of parameters of interest of actual device structures based on optical measurements of nearby metrology targets are presented herein. High throughput, inline metrology techniques are employed to measure metrology targets located near actual device structures. Measurement data collected from the metrology targets is provided to a trained signal response metrology (SRM) model. The trained SRM model estimates the value of one or more parameters of interest of the actual device structure based on the measurements of the metrology target. The SRM model is trained to establish a functional relationship between actual device parameters measured by a reference metrology system and corresponding optical measurements of at least one nearby metrology target. In a further aspect, the trained SRM is employed to determine corrections of process parameters to bring measured device parameter values within specification.

Abstract: Methods and systems for evaluating the performance of multiple patterning processes are presented. Patterned structures are measured and one or more parameter values characterizing geometric errors induced by the multiple patterning process are determined. In some examples, a single patterned target and a multiple patterned target are measured, the collected data fit to a combined measurement model, and the value of a structural parameter indicative of a geometric error induced by the multiple patterning process is determined based on the fit. In some other examples, light having a diffraction order different from zero is collected and analyzed to determine the value of a structural parameter that is indicative of a geometric error induced by a multiple patterning process. In some embodiments, a single diffraction order different from zero is collected. In some examples, a metrology target is designed to enhance light diffracted at an order different from zero.

Abstract: Methods and systems for performing simultaneous X-ray Fluorescence (XRF) and small angle x-ray scattering (SAXS) measurements over a desired inspection area of a specimen are presented. SAXS measurements combined with XRF measurements enables a high throughput metrology tool with increased measurement capabilities. The high energy nature of x-ray radiation penetrates optically opaque thin films, buried structures, high aspect ratio structures, and devices including many thin film layers. SAXS measurements of a particular location of a planar specimen are performed at a number of different out of plane orientations. This increases measurement sensitivity, reduces correlations among parameters, and improves measurement accuracy. In addition, specimen parameter values are resolved with greater accuracy by fitting data sets derived from both SAXS and XRF measurements based on models that share at least one material parameter. The fitting can be performed sequentially or in parallel.

Abstract: Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of spectra signals are measured from a particular structure of interest at a plurality of azimuth angles from one or more sensors of a metrology system. A difference spectrum is determined based on the spectra signals obtained for the azimuth angles. A quality indication of the particular structure of interest is determined and reported based on analyzing the difference spectrum.

Abstract: Structural parameters of a specimen are determined by fitting models of the response of the specimen to measurements collected by different measurement techniques in a combined analysis. X-ray measurement data of a specimen is analyzed to determine at least one specimen parameter value that is treated as a constant in a combined analysis of both optical measurements and x-ray measurements of the specimen. For example, a particular structural property or a particular material property, such as an elemental composition of the specimen, is determined based on x-ray measurement data. The parameter(s) determined from the x-ray measurement data are treated as constants in a subsequent, combined analysis of both optical measurements and x-ray measurements of the specimen. In a further aspect, the structure of the response models is altered based on the quality of the fit between the models and the corresponding measurement data.

Abstract: Methods and systems for evaluating the capability of a measurement system to track measurement parameters through a given process window are presented herein. Performance evaluations include random perturbations, systematic perturbations, or both to effectively characterize the impact of model errors, metrology system imperfections, and calibration errors, among others. In some examples, metrology target parameters are predetermined as part of a Design of Experiments (DOE). Estimated values of the metrology target parameters are compared to the known DOE parameter values to determine the tracking capability of the particular measurement. In some examples, the measurement model is parameterized by principal components to reduce the number of degrees of freedom of the measurement model. In addition, exemplary methods and systems for optimizing the measurement capability of semiconductor metrology systems for metrology applications subject to process variations are presented.

Abstract: Methods and systems for enhancing metrology sensitivity to particular parameters of interest are presented. Field enhancement elements (FEEs) are constructed as part of a specimen to enhance the measurement sensitivity of structures of interest present on the specimen. The design of the FEEs takes into account measurement goals and manufacturing design rules to make target fabrication compatible with the overall device fabrication process. Measurement of opaque materials, high-aspect ratio structures, structures with low-sensitivity, or mutually correlated parameters is enhanced by the addition of FEEs. Exemplary measurements include critical dimension, film thickness, film composition, and optical scatterometry overlay. In some examples, a target element includes different FEEs to improve the measurement of different structures of interest. In other examples, different target elements include different FEEs.

Abstract: Various metrology systems and methods for high aspect ratio and large lateral dimension structures are provided. One method includes directing light to one or more structures formed on a wafer. The light includes ultraviolet light, visible light, and infrared light. The one or more structures include at least one high aspect ratio structure or at least one large lateral dimension structure. The method also includes generating output responsive to light from the one or more structures due to the light directed to the one or more structures. In addition, the method includes determining one or more characteristics of the one or more structures using the output.

Abstract: Model optimization approaches based on spectral sensitivity is described. For example, a method includes determining a first model of a structure. The first model is based on a first set of parameters. A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data. The second model of the structure is based on a second set of parameters different from the first set of parameters. A simulated spectrum derived from the second model of the structure is then provided.