Abstract: Various embodiments herein relate to systems, methods, and media for matching pre-processing and post-processing substrate samples.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Described herein are various methods and manufacturing methods for making antimicrobial and strengthened, antimicrobial glass articles and substrates. The methods described herein generally include contacting the article with a KNO3-containing molten salt bath set at about 380 C to about 460 C for about 30 minutes to about 24 hours to form a compressive stress layer that extends inward from a surface of the glass substrate to a first depth; and contacting the article comprising the compressive stress layer with a AgNO3-containing molten salt bath set at about 300° C. to about 400° C. for about 5 minutes to about 18 hours to form an antimicrobial region that extends inward from the surface of the glass substrate to a second depth. The methods also include poisoning at least the AgNO3-containing molten salt bath and, in some cases, the KNO3-containing molten salt bath. Poisoning components include Na+ and Li+ ions.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Abstract: Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Abstract: Disclosed are methods of optimizing a computerized model which relates etched feature profile on a semiconductor device to a set of independent input parameters via the use of a plurality of model parameters. The optimization methods may include modifying the model parameters so that an etch profile generated with the model is such that it reduces a metric indicative of the combined differences between experimental etch profiles resulting from experimental etch processes performed using different sets of values for sets of independent input parameters and computed etch profiles generated from the model and corresponding to the experimental etch profiles. Said metric may be calculated by projecting computed and corresponding experimental etch profiles onto a reduced-dimensional subspace used to calculate a difference between the profiles. Also disclosed herein are systems employing such optimized models, as well as methods of using such models to approximately determine the profile of an etched feature.

Abstract: Disclosed are methods of optimizing a computer model which relates the etch profile of a feature on a semiconductor substrate to a set of independent input parameters (A), via the use of a plurality of model parameters (B). In some embodiments, the methods may include modifying one or more values of B so as to reduce a metric indicative of the differences between computed reflectance spectra generated from the model and corresponding experimental reflectance spectra with respect to one or more sets of values of A. In some embodiments, calculating the metric may include an operation of projecting the computed and corresponding experimental reflectance spectra onto a reduced-dimensional subspace and calculating the difference between the reflectance spectra as projected onto the subspace. Also disclosed are etch systems implementing such optimized computer models.

Abstract: Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Abstract: A system and method of identifying a selected process point in a multi-mode pulsing process includes applying a multi-mode pulsing process to a selected wafer in a plasma process chamber, the multi-mode pulsing process including multiple cycles, each one of the cycles including at least one of multiple, different phases. At least one process output variable is collected for a selected at least one of the phases, during multiple cycles for the selected wafer. An envelope and/or a template of the collected at least one process output variable can be used to identify the selected process point. A first trajectory for the collected process output variable of a previous phase can be compared to a second trajectory of the process output variable of the selected phase. A multivariate analysis statistic of the second trajectory can be calculated and used to identify the selected process point.

Abstract: Disclosed are methods of optimizing a computer model which relates the etch profile of a feature on a semiconductor substrate to a set of independent input parameters (A), via the use of a plurality of model parameters (B). In some embodiments, the methods may include modifying one or more values of B so as to reduce a metric indicative of the differences between computed reflectance spectra generated from the model and corresponding experimental reflectance spectra with respect to one or more sets of values of A. In some embodiments, calculating the metric may include an operation of projecting the computed and corresponding experimental reflectance spectra onto a reduced-dimensional subspace and calculating the difference between the reflectance spectra as projected onto the subspace. Also disclosed are etch systems implementing such optimized computer models.

Abstract: Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Abstract: Disclosed are methods of optimizing a computer model which relates the etch profile of a feature on a semiconductor substrate to a set of independent input parameters (A), via the use of a plurality of model parameters (B). In some embodiments, the methods may include modifying one or more values of B so as to reduce a metric indicative of the differences between computed reflectance spectra generated from the model and corresponding experimental reflectance spectra with respect to one or more sets of values of A. In some embodiments, calculating the metric may include an operation of projecting the computed and corresponding experimental reflectance spectra onto a reduced-dimensional subspace and calculating the difference between the reflectance spectra as projected onto the subspace. Also disclosed are etch systems implementing such optimized computer models.

Abstract: Monitoring a geometric parameter value for one or more features produced on a substrate during an etch process may involve: (a) measuring optical signals produced by optical energy interacting with features being etched on the substrate; (b) providing a subset of the measured optical signals, wherein the subset is defined by a range where optical signals were determined to correlate with target geometric parameter values for features; (c) applying the subset of optical signals to a model configured to predict the target geometric parameter values from the measured optical signals; (d) determining, from the model, a current value of the target geometric parameter of the features being etched; (e) comparing the current value of the target geometric parameter of the features being etched to an etch process endpoint value for the target geometric parameter; and (f) repeating (a)-(e) until the comparing in (e) indicates that the current value of the target geometric parameter of the features being etched has reach

Abstract: Disclosed are methods of optimizing a computer model which relates the etch profile of a feature on a semiconductor substrate to a set of independent input parameters (A), via the use of a plurality of model parameters (B). In some embodiments, the methods may include modifying one or more values of B so as to reduce a metric indicative of the differences between computed reflectance spectra generated from the model and corresponding experimental reflectance spectra with respect to one or more sets of values of A. In some embodiments, calculating the metric may include an operation of projecting the computed and corresponding experimental reflectance spectra onto a reduced-dimensional subspace and calculating the difference between the reflectance spectra as projected onto the subspace. Also disclosed are etch systems implementing such optimized computer models.

Abstract: Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Abstract: Disclosed are methods of optimizing a computer model which relates the etch profile of a feature on a semiconductor substrate to a set of independent input parameters (A), via the use of a plurality of model parameters (B). In some embodiments, the methods may include modifying one or more values of B so as to reduce a metric indicative of the differences between computed reflectance spectra generated from the model and corresponding experimental reflectance spectra with respect to one or more sets of values of A. In some embodiments, calculating the metric may include an operation of projecting the computed and corresponding experimental reflectance spectra onto a reduced-dimensional subspace and calculating the difference between the reflectance spectra as projected onto the subspace. Also disclosed are etch systems implementing such optimized computer models.