Abstract: Methods and systems for measuring values of one or more parameters of interest, including changes in values of one or more parameters of interest, based on measured spectral differences are presented herein. A trained spectral difference based measurement model determines changes in the values of one or more parameters of interest based on a measure of differences in spectra measured before and after one or more process steps. In some examples, a measure of spectral difference is determined based on a difference in measured intensity, a difference in harmonic signal values, or a difference in value of one or more Mueller Matrix elements. A measure of spectral difference may be expressed as a set of difference values, a scalar value, or coefficients of a functional fit to difference values. A measure of spectral difference may be determined based on a weighting of spectral differences according to wavelength.

Abstract: Methods and systems for estimating values of parameters of interest from optical measurements of a sample early in a production flow based on a measurement condition dependent, multidimensional optical dispersion (MCD-MDOD) model are presented herein. A MCD-MDOD model includes a multi-dimensional parametric model characterizing modelled parameters of interest as dependent on one or more measurement condition parameters. In this manner, the MCD-MDOD model captures the dependency of dispersion properties of the measured material on one or more measurement conditions, e.g., temperature, humidity, pressure, nitrogen purge condition, deformation, material processing conditions, radial location on wafer, coordinate location on wafer, etc. In some measurement applications, measurement accuracy is improved and the computational effort required to develop the MCD-MDOD model is less than alternative techniques.

Abstract: A thickness of a film stack of a workpiece and a composition of the film stack is determined with an x-ray technique, spectroscopic ellipsometry (SE) and/or spectroscopic reflectometry (SR), and multiwavelength Raman spectroscopy. The measurements are combined to form combined measured data. The combining includes regressing the second measurements and the third measurements. The thickness of the film stack and the composition of the film stack is then determined using the combined measured data.

Abstract: Methods and systems for improved detection of defects of interest and measurement of structures buried within complex three dimensional semiconductor structures are described herein. Through-focus scanning optical microscopy (TSOM) using non-linear, second harmonic generation (SHG) light signals emitted from a sample provides interface-selective sensitivity for metrology and inspection of advanced semiconductor structures. A TSOM/SHG system includes a spectral filter to pass collected light at wavelengths corresponding to SHG emission. In some embodiments, a TSOM/SHG system includes an ultrafast, pulsed laser source emitting ultraviolet to near infrared wavelengths to efficiently induce SHG at surface interfaces. The halving of wavelength inherent to SHG enables a doubling of illumination wavelength without penalizing resolution.

Abstract: An inspection system may perform an inspection recipe by receiving reference SHG images of a reference structure based on a scan of the reference structure with an illumination beam and collecting second harmonic generation (SHG) light in response to the illumination beam, where the reference structure includes a multilayer structure including one or more inversion-symmetric materials. The system may further receive test SHG images of a test structure based on a scan of the test structure by illuminating the test structure with the illumination beam and collecting the SHG light in response to the illumination beam, where the test structure and the reference structure have a common design. The system may further identify defects in the test structure by comparing the test and reference SHG images.

Abstract: Methods and systems for measuring values of one or more parameters of interest, including changes in values of one or more parameters of interest, based on measured spectral differences are presented herein. A trained spectral difference based measurement model determines changes in the values of one or more parameters of interest based on a measure of differences in spectra measured before and after one or more process steps. In some examples, a measure of spectral difference is determined based on a difference in measured intensity, a difference in harmonic signal values, or a difference in value of one or more Mueller Matrix elements. A measure of spectral difference may be expressed as a set of difference values, a scalar value, or coefficients of a functional fit to difference values. A measure of spectral difference may be determined based on a weighting of spectral differences according to wavelength.

Abstract: A metrology system may include an illumination source to generate an illumination beam and an illumination sub-system to direct the illumination beam to a sample with an inversion-symmetric substrate and one or more films disposed on the inversion-symmetric substrate. The system may further include a filter configured to block a wavelength of the illumination beam and pass a wavelength associated with a second harmonic of the illumination beam and a detector to capture second harmonic generation (SHG) light. The system may further include a controller to receive metrology data from the detector associated with the SHG light from with an interface between the inversion-symmetric substrate and the one or more films and generate one or more metrology measurements associated with the one or more films based on the metrology data.

Abstract: Methods and systems for compensating systematic errors across a fleet of metrology systems based on a trained error evaluation model to improve matching of measurement results across the fleet are described herein. In one aspect, the error evaluation model is a machine learning based model trained based on a set of composite measurement matching signals. Composite measurement matching signals are generated based on measurement signals generated by each target measurement system and corresponding model-based measurement signals associated with each target measurement system and reference measurement system. The training data set also includes an indication of whether each target system is operating within specification, an indication of the values of system model parameter of each target system, or both.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: Methods of precisely analyzing and modeling band gap energies and electrical properties of a thin film are provided. One method includes: obtaining a substrate and a thin film disposed above the substrate, the thin film including an interfacial layer above the substrate, and a high-k layer above the interfacial layer; determining a thickness of the thin film; analyzing the thin film using deep ultraviolet spectroscopy ellipsometry to determine the photon energy of reflected light; using a model to determine a set of bandgap energies extracted from a set of results of the photon energy of the analyzing step; and determining at least one of: a leakage current from a main bandgap energy, a nitrogen content from a sub bandgap energy, and an equivalent oxide thickness from the nitrogen content and a composition of the interfacial layer.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.

Abstract: Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.

Abstract: The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.