Abstract: A process to model post-exposure effects in patterning processes, the process including: obtaining values based on measurements of structures formed on one or more substrates by a post-exposure process and values of a pair of process parameters by which process conditions were varied; modeling, by a processor system, as a surface, correlation between the values based on measurements of the structures and the values of the pair of process parameters; and storing the model in memory.

Abstract: Methods of determining, and using, a process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: A method for improving a process model for a patterning process, the method including obtaining a) a measured contour from an image capture device, and b) a simulated contour generated from a simulation of the process model. The method also includes aligning the measured contour with the simulated contour by determining an offset between the measured contour and the simulated contour. The process model is calibrated to reduce a difference, computed based on the determined offset, between the simulated contour and the measured contour.

Abstract: A method including: obtaining a relationship between a performance indicator of a substrate measurement recipe and a parameter of the substrate measurement recipe; deriving a range of the parameter from the relationship, wherein absolute values of the performance indicator satisfy a first condition or a magnitude of variation of the performance indicator satisfies a second condition, when the first parameter is in the range; selecting a substrate measurement recipe that has the parameter in the range; and inspecting a substrate with the selected substrate measurement recipe.

Abstract: Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: A method including computing a multi-variable cost function, the multi-variable cost function representing a metric characterizing a degree of matching between a result when measuring a metrology target structure using a substrate measurement recipe and a behavior of a pattern of a functional device, the metric being a function of a plurality of design variables including a parameter of the metrology target structure, and adjusting the design variables and computing the cost function with the adjusted design variables, until a certain termination condition is satisfied.

Abstract: Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: Methods of constructing a process model for simulating a characteristic of a product of lithography from patterns produced under different processing conditions. The methods use a deviation between the variation of the simulated characteristic and the variation of the measured characteristic to adjust a parameter of the process model.

Abstract: A method involving determining an etch bias for a pattern to be etched using an etch step of a patterning process based on an etch bias model, the etch bias model including a formula having a variable associated with a spatial property of the pattern or with an etch plasma species concentration of the etch step, and including a mathematical term including a natural exponential function to the power of a parameter that is fitted or based on an etch time of the etch step; and adjusting the patterning process based on the determined etch bias.

Abstract: A method including: obtaining a relationship between a performance indicator of a substrate measurement recipe and a parameter of the substrate measurement recipe; deriving a range of the parameter from the relationship, wherein absolute values of the performance indicator satisfy a first condition or a magnitude of variation of the performance indicator satisfies a second condition, when the first parameter is in the range; selecting a substrate measurement recipe that has the parameter in the range; and inspecting a substrate with the selected substrate measurement recipe.

Abstract: A method including computing a multi-variable cost function, the multi-variable cost function representing a metric characterizing a degree of matching between a result when measuring a metrology target structure using a substrate measurement recipe and a behavior of a pattern of a functional device, the metric being a function of a plurality of design variables including a parameter of the metrology target structure, and adjusting the design variables and computing the cost function with the adjusted design variables, until a certain termination condition is satisfied.

Abstract: Methods and systems for automatically generating robust metrology targets which can accommodate a variety of lithography processes and process perturbations. Individual steps of an overall lithography process are modeled into a single process sequence to simulate the physical substrate processing. That process sequence drives the creation of a three-dimensional device geometry as a whole, rather than “building” the device geometry element-by-element.

Abstract: Methods and systems for automatically generating robust metrology targets which can accommodate a variety of lithography processes and process perturbations. Individual steps of an overall lithography process are modeled into a single process sequence to simulate the physical substrate processing. That process sequence drives the creation of a three-dimensional device geometry as a whole, rather than “building” the device geometry element-by-element.

Abstract: Methods and systems for automatically generating robust metrology targets which can accommodate a variety of lithography processes and process perturbations. Individual steps of an overall lithography process are modeled into a single process sequence to simulate the physical substrate processing. That process sequence drives the creation of a three-dimensional device geometry as a whole, rather than “building” the device geometry element-by-element.

Abstract: A method to determine an overlay error between a first structure and a second structure, wherein the first structure and second structures are on different layers on a substrate and are imaged onto the substrate by a lithographic process, the method comprising: obtaining an apparent overlay error; obtaining a systematic error caused by a factor other than misalignment of the first and second structures; and determining the overlay error by removing the systematic error from the apparent overlay error. The method may alternatively comprise obtaining apparent characteristics of diffraction orders of diffraction by an overlapping portion of the first and second structures; obtaining corrected characteristics of the diffraction orders; determining the overlay error from the corrected characteristics; and adjusting a characteristic of the lithographic process based on the overlay error.

Abstract: A method including performing a simulation to evaluate a plurality of metrology targets and/or a plurality of metrology recipes used to measure a metrology target, identifying one or more metrology targets and/or metrology recipes from the evaluated plurality of metrology targets and/or metrology recipes, receiving measurement data of the one or more identified metrology targets and/or metrology recipes, and using the measurement data to tune a metrology target parameter or metrology recipe parameter.

Abstract: A method of metrology target design is described. The method includes determining a sensitivity of a parameter for a metrology target design to an optical aberration, determining the parameter for a product design exposed using an optical system of a lithographic apparatus, and determining an impact on the parameter of the metrology target design based on the parameter for the product design and the product of the sensitivity and one or more of the respective aberrations of the optical system.

Abstract: A method of controlling a lithographic apparatus, the method including setting an illumination system of the lithographic apparatus to effect a selected illumination mode, measuring a value of a first parameter of the lithographic apparatus, calculating a value of a second parameter of a projected image of a feature of a test pattern having a plurality of features using a model of the lithographic apparatus and the measured value of the first parameter, and controlling the lithographic apparatus with reference to the calculated value of the second parameter.

Abstract: A system to, and a method to, select a metrology target for use on a substrate including performing a lithographic simulation for a plurality of points on a process window region for each proposed target, identifying a catastrophic error for any of the plurality of points for each proposed target, eliminating each target having a catastrophic error at any of the plurality of points, performing a metrology simulation to determine a parameter over the process window for each target not having a catastrophic error at any of the plurality of points, and using the one or more resulting determined simulated parameters to evaluate target quality.

Abstract: A method of metrology target design is described. The method includes determining a sensitivity of a parameter for a metrology target design to an optical aberration, determining the parameter for a product design exposed using an optical system of a lithographic apparatus, and determining an impact on the parameter of the metrology target design based on the parameter for the product design and the product of the sensitivity and one or more of the respective aberrations of the optical system.