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: Methods and systems for estimating values of parameters of interest from optical measurements of a sample early in a production flow based on a multidimensional optical dispersion (MDOD) model are presented herein. An MDOD model describes optical dispersion of materials comprising a structure under measurement in terms of parameters external to a base optical dispersion model. In some examples, a power law model describes the physical relationship between the external parameters and a parameter of the base optical dispersion model. In some embodiments, one or more external parameters are treated as unknown values that are resolved based on spectral measurement data. In some embodiments, one or more external parameters are treated as known values, and values of base optical dispersion model parameters, one or more external parameters having unknown values, or both, are resolved based on spectral measurement data and the known values of the one or more external parameters.

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 multidimensional optical dispersion (MDOD) model are presented herein. An MDOD model describes optical dispersion of materials comprising a structure under measurement in terms of parameters external to a base optical dispersion model. In some examples, a power law model describes the physical relationship between the external parameters and a parameter of the base optical dispersion model. In some embodiments, one or more external parameters are treated as unknown values that are resolved based on spectral measurement data. In some embodiments, one or more external parameters are treated as known values, and values of base optical dispersion model parameters, one or more external parameters having unknown values, or both, are resolved based on spectral measurement data and the known values of the one or more external parameters.

Abstract: Apparatus and methods for performing optically based film thickness measurements of highly absorbing films (e.g., high-K dielectric films) with improved measurement sensitivity are described herein. A highly absorbing film layer is fabricated on top of a highly reflective film stack. The highly reflective film stack includes one or more nominally identical sets of multiple layers of different, optically contrasting materials. The highly reflective film stack gives rise to optical resonance in particular wavelength ranges. The high reflectance at the interface of the highly absorbing film layer and the highly reflective film stack increases measured light intensity and measurement sensitivity. The thickness and optical dispersion of the different material layers of the highly reflective film stack are selected to induce optical resonance in a desired wavelength range. The desired wavelength range is selected to minimize absorption by the highly absorbing film under measurement.

Abstract: Apparatus and methods for performing optically based film thickness measurements of highly absorbing films (e.g., high-K dielectric films) with improved measurement sensitivity are described herein. A highly absorbing film layer is fabricated on top of a highly reflective film stack. The highly reflective film stack includes one or more nominally identical sets of multiple layers of different, optically contrasting materials. The highly reflective film stack gives rise to optical resonance in particular wavelength ranges. The high reflectance at the interface of the highly absorbing film layer and the highly reflective film stack increases measured light intensity and measurement sensitivity. The thickness and optical dispersion of the different material layers of the highly reflective film stack are selected to induce optical resonance in a desired wavelength range. The desired wavelength range is selected to minimize absorption by the highly absorbing film under measurement.

Abstract: A method for determining chemical composition from optical properties of a stack formed with a process, by preparing test samples of the stack using known and intentional variations to the process to affect a variation in the chemical composition, measuring the optical properties of the test samples, measuring the chemical composition of the test samples, performing a processor-based regression analysis to determine an optical state function including correlations between the optical properties of the test samples and the chemical composition of the test samples, fabricating production samples of the stack using the process, measuring the optical properties of the production samples, and estimating the chemical composition of the production samples using the optical state function.

Abstract: A method for determining chemical composition from optical properties of a stack formed with a process, by preparing test samples of the stack using known and intentional variations to the process to affect a variation in the chemical composition, measuring the optical properties of the test samples, measuring the chemical composition of the test samples, performing a processor-based regression analysis to determine an optical state function including correlations between the optical properties of the test samples and the chemical composition of the test samples, fabricating production samples of the stack using the process, measuring the optical properties of the production samples, and estimating the chemical composition of the production samples using the optical state function.

Abstract: A method and device for facilitating measurement of thermo-optically induced material phase change response in a thin planar or a grating film stack is disclosed. The method may include using small-spot visible and ultraviolet spectra (ellipsometric or reflectance) for measuring a material phase change response. The device may include a measurement system platform, at least one electrical resistor, at least one external electric probe, and ohmic contact circuitry.

Abstract: A method and device for facilitating measurement of thermo-optically induced material phase change response in a thin planar or a grating film stack is disclosed. The method may include using small-spot visible and ultraviolet spectra (ellipsometric or reflectance) for measuring a material phase change response. The device may include a measurement system platform, at least one electrical resistor, at least one external electric probe, and ohmic contact circuitry.

Abstract: A global node optimization (GNO) technique can generate a model for a planar multiple layer film stack structure, e.g. a binary grating structure. In this technique, after obtaining spectra and target thicknesses from one or more wafers, a continuous film approximation (CFA) and a grating factor (GF) set are identified. A model using the CFA and the GF set is optimized by simultaneously fitting a plurality of the spectra while minimizing error compared to the target thicknesses. After simultaneously fitting all of the spectra, a GNO stack is created. A GNO recipe is then created using the GNO stack. Notably, a tool implementing the GNO technique uses minimal modeling capabilities and computational resources.

Abstract: An optical metrology system includes model approximation logic for generating an optical model based on experimental data. By eliminating theoretical model generation, in which the fundamental equations of a test sample must be solved, the model approximation logic significantly reduces the computational requirements of the metrology system when measuring films formed on patterned base layers. The experimental model can be created by selecting an expected mathematical form for the final model, gathering experimental data, and compiling a lookup model. The lookup model can include the actual measurement data sorted by output (attribute) value, or can include “grating factors” that represent compensation factors that, when applied to standard monolithic model equations, compensate for the optical effects of grating layers.