Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Abstract: A method including obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

Abstract: A method for determining a metric of a feature on a substrate obtained by a semiconductor manufacturing process involving a lithographic process, the method including: obtaining an image of at least part of the substrate, wherein the image includes at least the feature; determining a contour of the feature from the image; determining a plurality of segments of the contour; determining respective weights for each of the plurality of segments; determining, for each of the segments, an image-related metric; and determining the metric of the feature in dependence on the weights and the calculated image-related metric of each of the segments.

Abstract: A method including obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

Abstract: A method including: obtaining an image of at least part of a substrate, wherein the image includes at least one feature of a device being manufactured in a layer on the substrate; obtaining a layout of features associated with a previous layer adjacent to the layer on the substrate; calculating one or more image-related metrics in dependence on: 1) a contour determined from the image including the at least one feature and 2) the layout; and determining one or more control parameters of a lithographic apparatus and/or one or more further processes in a manufacturing process of the device in dependence on the one or more image-related metrics, wherein at least one of the control parameters is determined to modify the geometry of the contour in order to improve the one or more image-related metrics.

Abstract: Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.

Abstract: Methods related to improving a simulation processes and solutions (e.g., retargeted patterns) associated with manufacturing of a chip. A method includes obtaining a plurality of dose-focus settings, and a reference distribution based on measured values of a characteristic of a printed pattern associated with each setting of the plurality of dose-focus settings. The method further includes, based on an adjustment model and the plurality of dose-focus settings, determining a probability density function (PDF) of the characteristic such that an error between the PDF and the reference distribution is reduced. The PDF can be a function of the adjustment model and variance associated with dose, the adjustment model being configured to change a proportion of non-linear dose sensitivity contribution to the PDF. A process window can be adjusted based on the determined PDF of the characteristic.

Abstract: A method for determining a metric of a feature on a substrate obtained by a semiconductor manufacturing process involving a lithographic process, the method including: obtaining an image of at least part of the substrate, wherein the image includes at least the feature; determining a contour of the feature from the image; determining a plurality of segments of the contour; determining respective weights for each of the plurality of segments; determining, for each of the segments, an image-related metric; and determining the metric of the feature in dependence on the weights and the calculated image-related metric of each of the segments.

Abstract: A method for determining a metric of a feature on a substrate obtained by a semiconductor manufacturing process involving a lithographic process, the method including: obtaining an image of at least part of the substrate, wherein the image includes at least the feature; determining a contour of the feature from the image; determining a plurality of segments of the contour; determining respective weights for each of the plurality of segments; determining, for each of the segments, an image-related metric; and determining the metric of the feature in dependence on the weights and the calculated image-related metric of each of the segments.

Abstract: Described herein is a method of training a model configured to predict whether a feature associated with an imaged substrate will be defective after etching of the imaged substrate and determining etch conditions based on the trained model. The method includes obtaining, via a metrology tool, (i) an after development image of the imaged substrate at a given location, the after development image including a plurality of features, and (ii) an after etch image of the imaged substrate at the given location; and training, using the after development image and the after etch image, the model configured to determine defectiveness of a given feature of the plurality of features in the after development image. In an embodiment, the determining of defectiveness is based on comparing the given feature in the after development image with a corresponding etch feature in the after etch image.

Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Abstract: A method including obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

Abstract: A method for controlling a processing apparatus used in a semiconductor manufacturing process to form a structure on a substrate, the method including: obtaining a relationship between a geometric parameter of the structure and a performance characteristic of a device including the structure; and determining a process setting for the processing apparatus associated with a location on the substrate, wherein the process setting is at least partially based on an expected value of the geometric parameter of the structure when using the processing setting, a desired performance characteristic of the device and an expected physical yield margin or defect yield margin associated with the location on the substrate.

Abstract: A method including obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

Abstract: A method for determining a metric of a feature on a substrate obtained by a semiconductor manufacturing process involving a lithographic process, the method including: obtaining an image of at least part of the substrate, wherein the image includes at least the feature; determining a contour of the feature from the image; determining a plurality of segments of the contour; determining respective weights for each of the plurality of segments; determining, for each of the segments, an image-related metric; and determining the metric of the feature in dependence on the weights and the calculated image-related metric of each of the segments.

Abstract: A method including obtaining (i) measurements of a parameter of the feature, (ii) data related to a process variable of a patterning process, (iii) a functional behavior of the parameter defined as a function of the process variable based on the measurements of the parameter and the data related to the process variable, (iv) measurements of a failure rate of the feature, and (v) a probability density function of the process variable for a setting of the process variable, converting the probability density function of the process variable to a probability density function of the parameter based on a conversion function, where the conversion function is determined based on the function of the process variable, and determining a parameter limit of the parameter based on the probability density function of the parameter and the measurements of the failure rate.

Abstract: A method for controlling a processing apparatus used in a semiconductor manufacturing process to form a structure on a substrate, the method including: obtaining a relationship between a geometric parameter of the structure and a performance characteristic of a device including the structure; and determining a process setting for the processing apparatus associated with a location on the substrate, wherein the process setting is at least partially based on an expected value of the geometric parameter of the structure when using the processing setting, a desired performance characteristic of the device and an expected physical yield margin or defect yield margin associated with the location on the substrate.