A method for a metrology process, the method includes obtaining first measurement data relating to a first set of measurement conditions and determining a first measurement recipe based on the first measurement data. At least one performance indicator is determined from one or more components of the first measurement data obtained from a component analysis or statistical decomposition. Alternatively, at least one performance indicator is determined from a comparison of one or more first measurement values relating to the first measurement recipe and one or more second measurement values relating to a second measurement recipe, where second measurement recipe is different to the first measurement data and relates a second set of measurement conditions, the second set of measurement conditions being different to the first set of measurement conditions.

Displacement measurement systems, measurement methods and EUV lithography machines for quantum meta-structure elements are disclosed, which include: an entangled state light source generator, a meta-device, a collimating lens, a polarizing beam splitter, and a computing section. Left-rotation photon and the right-rotation photon are projected to the polarizing beam splitter, and the correlation between output ports of two polarizing beam splitters is counted for reading to measure the displacement of the meta-device element. The displacement of the meta-structure element is measured in a manner that realizes the correlation between the left-rotation photons and the right-rotation photons.

A method for overlay metrology may include generating broadband illumination beams and directing the broadband illumination beams to an overlay target on a sample, where the overlay target may include cells having periodic features formed as overlapping grating structures. The method may include generating diffracted light using the periodic features of the overlay target, where the periodic features may act as a diffraction grating to generate diffracted light by separating the broadband illumination beam into a plurality of wavelengths. The method may include generating pupil images of cells of the overlay target, where a distribution of light in the pupil plane may include first-order diffraction lobes, where spectra of the first-order diffraction lobes may be spatially dispersed in the pupil plane. The method may include generating an overlay measurement based on portions of the pupil images corresponding to selected wavelengths of the spectra of the first-order diffraction lobes.

A method includes identifying a printing location of a first set of alignment marks on a wafer outside a geometric shadow defined by a numerical aperture and a die height of a die. The method includes fabricating an overlay metrology target by printing the first set of alignment marks based on the identified print location and printing a second set of alignment marks on a surface of the die. The method includes identifying a metrology recipe including a set of measurement parameters, where the set of measurement parameters include at least a focal position of an objective lens. The focal position of the objective lens may be a predetermined distance below a top surface of the die defined by a maximal numerical aperture and the die height of the die. The method includes measuring overlay of the overlay metrology target in accordance with the identified metrology recipe.

Methods and systems for generating measurement models of complex semiconductor structures based on re-useable, parametric models are presented herein. In some embodiments, the re-useable, parametric models enable measurement of high aspect ratio (HAR) structures having complex shape profiles. In these embodiments, a re-useable, parametric model includes multiple geometric sections each characterized by a different shape profile. Each shape profile is parameterized by at least one shape parameter. In a further aspect, at least one of the multiple geometric sections includes a plurality of subsections. In some other embodiments, the re-useable, parametric models enable measurement of nanowire based semiconductor structures. The re-useable, parametric models described herein are useful for generating measurement models for both optical metrology and x-ray metrology, e.g., soft x-ray metrology and hard x-ray metrology. The resulting measurement models yield more accurate measurement results with improved robustness.

A method for determining a measurement setting for measuring a parameter of interest from a target structure on a substrate. The method includes: obtaining first position difference data describing a difference between a position of a first representative target structure position and a position of one or more first features relating to product structure; obtaining optical metrology data relating to optical measurements of the target structure and further relating to a plurality of different measurement settings; and determining the measurement setting from the first position difference data and the optical metrology data such that a measured feature position value obtained from an optical measurement of the target structure using the determined measurement setting is better correlated to a position of the one or more first features.

A method for determining a parameter of interest relating to at least one structure formed on a substrate in a manufacturing process. The method includes: obtaining layout data relating to a layout of a pattern to be applied to the at least one structure, the pattern including the at least one structure; and obtaining a trained model, having been trained on metrology data and the layout data to infer a value and/or probability metric relating to a parameter of interest from at least the layout data, the metrology data relating to a plurality of measurements of the parameter of interest at a respective plurality of measurement locations on the substrate. A value and/or probability metric is determined relating to the parameter of interest at one or more locations on the substrate different from the measurement locations from at least layout data using the trained model.

Displacement measurement systems, measurement methods and EUV lithography machines for quantum meta-structure elements are disclosed, which include: an entangled state light source generator, a meta-device, a collimating lens, a polarizing beam splitter, and a computing section. Left-rotation photon and the right-rotation photon are projected to the polarizing beam splitter, and the correlation between output ports of two polarizing beam splitters is counted for reading to measure the displacement of the meta-device element. The displacement of the meta-structure element is measured in a manner that realizes the correlation between the left-rotation photons and the right-rotation photons.

The present disclosure provides a method and a system for obtaining OCD. The system inputs a plurality of first spectra associated with a first semiconductor structure into the first optical transformation model to output a plurality of first structure parameters; update the first structure parameters based on at least one physical parameter associated with the first semiconductor structure; establish a second optical transformation model according to the updated first structure parameters and the corresponding first spectra; and store the second optical transformation model for later OCD generation.

A method for determining a measurement recipe for measuring a parameter of interest from a compound structure on a substrate. The method includes obtaining first training data relating to measurements of reference targets, the targets including: parameter of interest targets, each parameter of interest target having an induced set value which is varied over the parameter of interest targets; and one or more isolated feature targets, each including repetitions of one or more features. Second training data is obtained, the second training data including compound structure measurement signals obtained from measurement of one or more instances of the compound structure. One or more machine learning models are trained using the first training data and second training data to infer a value for the parameter of interest from a measurement signal related to the compound structure corrected for a feature asymmetry contribution.