Abstract: A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

Abstract: A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

Abstract: A method for selecting an optimal set of locations for a measurement or feature on a substrate, the method includes: defining a first candidate solution of locations, defining a second candidate solution with locations based on modification of a coordinate in a solution domain of the first candidate solution, and selecting the first and/or second candidate solution as the optimal solution according to a constraint associated with the substrate.

Abstract: A method at tuning a lithographic process for a particular patterning device. The method includes: obtaining wavefront data relating to an objective lens of a lithographic apparatus, the wavefront data measured subsequent to an exposure of a pattern on a substrate using the particular patterning device; determining a pattern specific wavefront contribution from the wavefront data and a wavefront reference, the pattern specific wavefront contribution relating to the patterning device; and tuning the lithographic process for the particular patterning device using the pattern specific wavefront contribution.

Abstract: A method for selecting an optimal set of locations for a measurement or feature on a substrate, the method includes: defining a first candidate solution of locations, defining a second candidate solution with locations based on modification of a coordinate in a solution domain of the first candidate solution, and selecting the first and/or second candidate solution as the optimal solution according to a constraint associated with the substrate.

Abstract: A method for selecting an optimal set of locations for a measurement or feature on a substrate, the method includes: defining a first candidate solution of locations, defining a second candidate solution with locations based on modification of a coordinate in a solution domain of the first candidate solution, and selecting the first and/or second candidate solution as the optimal solution according to a constraint associated with the substrate.

Abstract: A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

Abstract: A method comprising determining aberrations caused by each lithographic apparatus of a set of lithographic apparatuses, calculating adjustments of the lithographic apparatuses which minimize differences between the aberrations caused by each of the lithographic apparatuses, and applying the adjustments to the lithographic apparatuses, providing better matching between the aberrations of patterns projected by the lithographic apparatuses.

Abstract: A method comprising determining aberrations caused by each lithographic apparatus of a set of lithographic apparatuses, calculating adjustments of the lithographic apparatuses which minimize differences between the aberrations caused by each of the lithographic apparatuses, and applying the adjustments to the lithographic apparatuses, providing better matching between the aberrations of patterns projected by the lithographic apparatuses.

Abstract: A projection system model is configured to predict optical aberrations of a projection system based upon a set of projection system characteristics and to determine and output a set of optical element adjustments based upon a merit function. The merit function comprises a set of parameters and corresponding weights. The method comprises receiving an initial merit function and executing an optimization algorithm to determine a second merit function. The optimization algorithm scores different merit functions based upon projection system characteristics of a projection system adjusted according to the output of the projection system model using a merit function having that set of parameters and weights.

Abstract: A method for selecting an optimal set of locations for a measurement or feature on a substrate, the method includes: defining a first candidate solution of locations, defining a second candidate solution with locations based on modification of a coordinate in a solution domain of the first candidate solution, and selecting the first and/or second candidate solution as the optimal solution according to a constraint associated with the substrate.

Abstract: A projection system model is configured to predict optical aberrations of a projection system based upon a set of projection system characteristics and to determine and output a set of optical element adjustments based upon a merit function. The merit function comprises a set of parameters and corresponding weights. The method comprises receiving an initial merit function and executing an optimization algorithm to determine a second merit function. The optimization algorithm scores different merit functions based upon projection system characteristics of a projection system adjusted according to the output of the projection system model using a merit function having that set of parameters and weights.