Abstract: Methods and tools for generating measurement models of complex device structures based on re-useable, parametric models are presented. Metrology systems employing these models are configured to measure structural and material characteristics associated with different semiconductor fabrication processes. The re-useable, parametric sub-structure model is fully defined by a set of independent parameters entered by a user of the model building tool. All other variables associated with the model shape and internal constraints among constituent geometric elements are pre-defined within the model. In some embodiments, one or more re-useable, parametric models are integrated into a measurement model of a complex semiconductor device. In another aspect, a model building tool generates a re-useable, parametric sub-structure model based on input from a user.

Abstract: Ellipsometry systems and ellipsometry data collection methods with improved stabilities are disclosed. In accordance with the present disclosure, multiple predetermined, discrete analyzer angles are utilized to collect ellipsometry data for a single measurement, and data regression is performed based on the ellipsometry data collected at these predetermined, discrete analyzer angles. Utilizing multiple discrete analyzer angles for a single measurement improves the stability of the ellipsometry system.

Abstract: Embodiments include automatic selection of sample values for optical metrology. An embodiment of a method includes providing a library parameter space for modeling of a diffracting structure using an optical metrology system; automatically determining by a processing unit a reduced sampling set from the library parameter space, wherein the reduced space is based on one or both of the following recommending a sampling shape based on an expected sample space usage, or recommending a sampling filter based on correlation between two or more parameters of the library parameter space; and generating a library for the optical metrology system using the reduced sampling set.

Abstract: Metrology may be implemented during semiconductor device fabrication by a) modeling a first measurement on a first test cell formed in a layer of a partially fabricated device; b) performing a second measurement on a second test cell in the layer; c) feeding information from the second measurement into the modeling of the first measurement; and after a lithography pattern has been formed on the layer including the first and second test cells, d) modeling a third and a fourth measurement on the first and second test cells respectively using information from a) and b) respectively.

Abstract: Ellipsometry systems and ellipsometry data collection methods with improved stabilities are disclosed. In accordance with the present disclosure, multiple predetermined, discrete analyzer angles are utilized to collect ellipsometry data for a single measurement, and data regression is performed based on the ellipsometry data collected at these predetermined, discrete analyzer angles. Utilizing multiple discrete analyzer angles for a single measurement improves the stability of the ellipsometry system.

Abstract: Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of models having varying combinations of floating and fixed critical parameters and corresponding simulated spectra is generated. Each model is generated to determine one or more critical parameters for unknown structures based on spectra collected from such unknown structures. It is determined which one of the models best correlates with each critical parameter based on reference data that includes a plurality of known values for each of a plurality of critical parameters and corresponding known spectra. For spectra obtained from an unknown structure using a metrology tool, different ones of the models are selected and used to determine different ones of the critical parameters of the unknown structure based on determining which one of the models best correlates with each critical parameter based on the reference data.

Abstract: Metrology may be implemented during semiconductor device fabrication by a) modeling a first measurement on a first test cell formed in a layer of a partially fabricated device; b) performing a second measurement on a second test cell in the layer; c) feeding information from the second measurement into the modeling of the first measurement; and after a lithography pattern has been formed on the layer including the first and second test cells, d) modeling a third and a fourth measurement on the first and second test cells respectively using information from a) and b) respectively.

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: Automatic wavelength or angle pruning for optical metrology is described. An embodiment of a method for automatic wavelength or angle pruning for optical metrology includes determining a model of a structure including a plurality of parameters; designing and computing a dataset of wavelength-dependent or angle-dependent data for the model; storing the dataset in a computer memory; performing with a processor an analysis of the dataset for the model including applying an outlier detection technology on the dataset, and identifying any data outliers, each data outlier being a wavelength or angle; and, if any data outliers are identified in the analysis of the dataset of the model, removing the wavelengths or angles corresponding to the data outliers from the dataset to generate a modified dataset, and storing the modified dataset in the computer memory.

Abstract: Dynamic removal of correlation of highly-correlated parameters for optical metrology is described. An embodiment of a method includes determining a model of a structure, the model including a set of parameters; performing optical metrology measurement of the structure, including collecting spectra data on a hardware element; during the measurement of the structure, dynamically removing correlation of two or more parameters of the set of parameters, an iteration of the dynamic removal of correlation including: generating a Jacobian matrix of the set of parameters, applying a singular value decomposition of the Jacobian matrix, selecting a subset of the set of parameters, and computing a direction of the parameter search based on the subset of parameters. If the model does not converge, performing one or more additional iterations of the dynamic removal of correlation until the model converges; and if the model does converge, reporting the results of the measurement.

Abstract: Automatic determination of Fourier harmonic order for computation of spectral information for diffraction structures described. An embodiment of a method includes automatically determining a Fourier harmonic order for computation of spectral information for periodic structures, wherein the determination of the Fourier harmonic order is based at least in part on the pitches in each of multiple directions of the periodic structures, material properties of the periodic structures, and characteristics of the periodic structures in which the materials are contained; and computing the spectral information for the periodic structures based at least in part on the determined Fourier harmonic order.

Abstract: Methods and systems for matching measurement spectra across one or more optical metrology systems are presented. The values of one or more system parameters used to determine the spectral response of a specimen to a measurement performed by a target metrology system are optimized. The system parameter values are optimized such that differences between measurement spectra generated by a reference system and the target system are minimized for measurements of the same metrology targets. Methods and systems for matching spectral errors across one or more optical metrology systems are also presented. A trusted metrology system measures the value of at least one specimen parameter to minimize model errors introduced by differing measurement conditions present at the time of measurement by the reference and target metrology systems. Methods and systems for parameter optimization based on low-order response surfaces are presented to reduce the compute time required to refine system calibration parameters.

Abstract: Electromagnetic modeling of finite structures and finite illumination for metrology and inspection are described herein. In one embodiment, a method for evaluating a diffracting structure involves providing a model of the diffracting structure. The method involves computing background electric or magnetic fields of an environment of the diffracting structure. The method involves computing scattered electric or magnetic fields from the diffracting structure using a scattered field formulation based on the computed background fields. The method further involves computing spectral information for the model of the diffracting structure based on the computed scattered fields, and comparing the computed spectral information for the model with measured spectral information for the diffracting structure. In response to a good model fit, the method involves determining a physical characteristic of the diffracting structure based on the model of the diffracting structure.

Abstract: An apparatus to calibrate a polarizer in a polarized optical system at any angle of incidence. The apparatus decouples the polarization effect of the system from the polarization effect of the sample. The apparatus includes a substrate with a polarizer disposed on the surface. An indicator on the substrate indicates the polarization orientation of the polarizer, which is in a predetermined orientation with respect to the substrate.

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 dispersion metrics are determined based on the spectral data. Band structure characteristics such as band gap, band edge, and defects are determined based on optical dispersion metric values. In some embodiments a band structure characteristic is determined by curve fitting and interpolation of dispersion metric values. In some other embodiments, band structure characteristics are determined by regression of a selected dispersion model. In some examples, band structure characteristics indicative of band broadening of high-k dielectric films are also determined. The electrical performance of finished wafers is estimated based on the band structure characteristics identified early in the manufacturing process.

Abstract: An apparatus to calibrate a polarizer in a polarized optical system at any angle of incidence. The apparatus decouples the polarization effect of the system from the polarization effect of the sample. The apparatus includes a substrate with a polarizer disposed on the surface. An indicator on the substrate indicates the polarization orientation of the polarizer, which is in a predetermined orientation with respect to the substrate.

Abstract: Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described.

Abstract: Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. In an example, a method includes optimizing a threshold for a principal component analysis (PCA) of a spectrum data set to provide a principal component (PC) value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the PC value provided from optimizing the threshold for the PCA, and providing a spectral library based on the one or more trained neural networks.

Abstract: A method to calibrate a polarizer in polarized optical system at any angle of incidence, by decoupling the calibration from a polarization effect of the system, by providing a calibration apparatus that includes a substrate having a polarizer disposed on a surface thereof, with an indicator on the substrate for indicating a polarization orientation of the polarizer, loading the calibration apparatus in the polarized optical system with the indicator in a desired position, determining an initial angle between the polarization orientation and a reference of the polarized optical system, acquiring spectra using the polarized optical system at a plurality of known angles between the polarization orientation and the reference of the polarized optical system, using the spectra to plot a curve indicating an angle of the polarizer in the polarized optical system, and when the angle of the polarizer is outside of a desired range, adjusting the angle of the polarizer, and repeating the steps of acquiring the spectra, a

Abstract: Methods of library generation with derivatives for optical metrology are described. For example, a method of generating a library for optical metrology includes determining a function of a parameter data set for one or more repeating structures on a semiconductor substrate or wafer. The method also includes determining a first derivative of the function of the parameter data set. The method also includes providing a spectral library based on both the function and the first derivative of the function.