Abstract: Methods of measuring variation across multiple instances of a pattern on a substrate or substrates after a step in a device manufacturing process are disclosed. In one arrangement, data representing a set of images is received. Each image represents a different instance of the pattern. The set of images are registered relative to each other to superimpose the instances of the pattern. Variation in the pattern is measured using the registered set of images. The pattern comprises a plurality of pattern elements and the registration comprises applying different weightings to two or more of the plurality of pattern elements. The weightings control the extent to which each pattern element contributes to the registration of the set of images. Each weighting is based on an expected variation of the pattern element to which the weighting is applied.

Abstract: A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data comprising an input image of at least a part of a substrate having a plurality of features and a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation of the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.

Abstract: Disclosed herein is an inspection tool and a method for identifying defects in a sample. The method includes steps of scanning a first area of a sample with a first detector-beam and scanning a second area of the sample with a second detector-beam, then receiving first and second signals that are derived from the first and second detector-beams. The first and second signals are compared to determine whether a defect is present in the sample.

Abstract: An apparatus and method to determine a property of a substrate by measuring, in the pupil plane of a high numerical aperture lens, an angle-resolved spectrum as a result of radiation being reflected off the substrate. The property may be angle and wavelength dependent and may include the intensity of TM- and TE-polarized radiation and their relative phase difference.

Abstract: A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data comprising an input image of at least a part of a substrate having a plurality of features and a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation of the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.

Abstract: A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data including an input image of at least a part of a substrate having a plurality of features and including a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation with the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.

Abstract: Methods of measuring variation across multiple instances of a pattern on a substrate or substrates after a step in a device manufacturing process are disclosed. In one arrangement, data representing a set of images is received. Each image represents a different instance of the pattern. The set of images are registered relative to each other to superimpose the instances of the pattern. Variation in the pattern is measured using the registered set of images. The pattern comprises a plurality of pattern elements and the registration comprises applying different weightings to two or more of the plurality of pattern elements. The weightings control the extent to which each pattern element contributes to the registration of the set of images. Each weighting is based on an expected variation of the pattern element to which the weighting is applied.

Abstract: A method for determining an image-metric of features on a substrate, the method including: obtaining a first image of a plurality of features on a substrate; obtaining one or more further images of a corresponding plurality of features on the substrate, wherein at least one of the one or more further images is of a different layer of the substrate than the first image; generating aligned versions of the first and one or more further images by performing an alignment process on the first and one or more further images; and calculating an image-metric in dependence on a comparison of the features in the aligned version of the first image and the corresponding features in the one or more aligned versions of the one or more further images.

Abstract: A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data including an input image of at least a part of a substrate having a plurality of features and including a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation with the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.

Abstract: Methods of measuring variation across multiple instances of a pattern on a substrate or substrates after a step in a device manufacturing process are disclosed. In one arrangement, data representing a set of images is received. Each image represents a different instance of the pattern, wherein the pattern includes a plurality of pattern elements. The set of images are registered relative to each other to superimpose the instances of the pattern. The registration includes applying different weightings to two or more of the plurality of pattern elements, wherein the weightings control the extent to which each pattern element contributes to the registration of the set of images and each weighting is based on an expected variation of the pattern element to which the weighting is applied. Variation in the pattern is measured using the registered set of images.

Abstract: An apparatus and method to determine a property of a substrate by measuring, in the pupil plane of a high numerical aperture lens, an angle-resolved spectrum as a result of radiation being reflected off the substrate. The property may be angle and wavelength dependent and may include the intensity of TM- and TE-polarized radiation and their relative phase difference.

Abstract: An apparatus and method to determine a property of a substrate by measuring, in the pupil plane of a high numerical aperture lens, an angle-resolved spectrum as a result of radiation being reflected off the substrate. The property may be angle and wavelength dependent and may include the intensity of TM- and TE-polarized radiation and their relative phase difference.

Abstract: Methods of measuring variation across multiple instances of a pattern on a substrate or substrates after a step in a device manufacturing process are disclosed. In one arrangement, data representing a set of images is received. Each image represents a different instance of the pattern, wherein the pattern includes a plurality of pattern elements. The set of images are registered relative to each other to superimpose the instances of the pattern. The registration includes applying different weightings to two or more of the plurality of pattern elements, wherein the weightings control the extent to which each pattern element contributes to the registration of the set of images and each weighting is based on an expected variation of the pattern element to which the weighting is applied. Variation in the pattern is measured using the registered set of images.

Abstract: An apparatus and method to determine a property of a substrate by measuring, in the pupil plane of a high numerical aperture lens, an angle-resolved spectrum as a result of radiation being reflected off the substrate. The property may be angle and wavelength dependent and may include the intensity of TM- and TE-polarized radiation and their relative phase difference.

Abstract: An apparatus and method to determine a property of a substrate by measuring, in the pupil plane of a high numerical aperture lens, an angle-resolved spectrum as a result of radiation being reflected off the substrate. The property may be angle and wavelength dependent and may include the intensity of TM- and TE-polarized radiation and their relative phase difference.

Abstract: A method to determine the usefulness of an alignment mark of a first pattern in transferring a second pattern to a substrate relative to the first pattern already present on the substrate includes measuring the position of the alignment mark, modeling the position of the alignment mark, determining the model error between measured and modeled position, measuring a corresponding overlay error between first and second pattern and comparing the model error with the overlay error to determine the usefulness of the alignment mark. Subsequently this information can be used when processing next substrates thereby improving the overlay for these substrates. A lithographic apparatus and/or overlay measurement system may be operated in accordance with the method.

Abstract: A method of, and associated apparatus for, determining focus corrections for a lithographic projection apparatus. The method comprises exposing a plurality of global correction fields on a test substrate, each comprising a plurality of global correction marks, and each being exposed with a tilted focus offset across it; measuring a focus dependent characteristic for each of the plurality of global correction marks to determine interfield focus variation information; and calculating interfield focus corrections from the interfield focus variation information.

Abstract: A method of, and associated apparatus for, determining focus corrections for a lithographic projection apparatus. The method comprises exposing a plurality of global correction fields on a test substrate, each comprising a plurality of global correction marks, and each being exposed with a tilted focus offset across it; measuring a focus dependent characteristic for each of the plurality of global correction marks to determine interfield focus variation information; and calculating interfield focus corrections from the interfield focus variation information.

Abstract: For angular resolved spectrometry a radiation beam is used having an illumination profile having four quadrants is used. The first and third quadrants are illuminated whereas the second and fourth quadrants aren't illuminated. The resulting pupil plane is thus also divided into four quadrants with only the zeroth order diffraction pattern appearing in the first and third quadrants and only the first order diffraction pattern appearing in the second and third quadrants.

Abstract: A method to determine the usefulness of an alignment mark of a first pattern in transferring a second pattern to a substrate relative to the first pattern already present on the substrate includes measuring the position of the alignment mark, modeling the position of the alignment mark, determining the model error between measured and modeled position, measuring a corresponding overlay error between first and second pattern and comparing the model error with the overlay error to determine the usefulness of the alignment mark. Subsequently this information can be used when processing next substrates thereby improving the overlay for these substrates. A lithographic apparatus and/or overlay measurement system may be operated in accordance with the method.