Abstract:
A method and apparatus of detection, registration and quantification of an image. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.
Abstract:
Systems and methods for predicting substrate geometry associated with a patterning process are described. Input information including geometry information and/or process information for a pattern is received and, using a machine learning prediction model, multi-dimensional output substrate geometry is predicted. The multi-dimensional output information may include pattern probability images. A stochastic edge placement error band and/or a stochastic failure rate may be predicted. The input information can include simulated aerial images, simulated resist images, target substrate dimensions, and/or data from a lithography apparatus associated with device manufacturing. Different aerial images may correspond to different heights in resist layers associated with the patterning process, for example.
Abstract:
A method for training a machine learning model configured to predict a substrate image corresponding to a printed pattern of a substrate as measured via a metrology tool. The method involves obtaining a training data set including (i) metrology data of the metrology tool used to measure the printed pattern of the substrate, and (ii) a representation of a mask pattern employed for imaging the printed pattern on the substrate; and training, based on the training data set, a machine learning model to predict the substrate image of the substrate as measured by the metrology tool such that a cost function is improved, wherein the cost function includes a relationship between the predicted substrate image and the metrology data.
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 of topography determination, the method including: obtaining a first focus value derived from a computational lithography model modeling patterning of an unpatterned substrate or derived from measurements of a patterned layer on an unpatterned substrate; obtaining a second focus value derived from measurement of a substrate having a topography; and determining a value of the topography from the first and second focus values.
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 determining a first color pattern and a second color pattern associated with a hot spot of a design layout pattern, the design layout pattern configured for transfer to a substrate, and predicting, by a hardware computer system, whether there would be a defect at the hot spot on the substrate caused by overlay error, based at least in part on a measurement of an overlay error between the first color pattern and the second color pattern.
Abstract:
A method of defect validation for a device manufacturing process, the method including: obtaining a first image of a pattern processed into an area on a substrate using the device manufacturing process under a first condition; obtaining a metrology image from the area; aligning the metrology image and the first image; and determining from the first image and the metrology image whether the area contains a defect, based on one or more classification criteria.
Abstract:
Disclosed is an inspection apparatus for use in lithography. It comprises a support for a substrate carrying a plurality of metrology targets; an optical system for illuminating the targets under predetermined illumination conditions and for detecting predetermined portions of radiation diffracted by the targets under the illumination conditions; a processor arranged to calculate from said detected portions of diffracted radiation a measurement of asymmetry for a specific target; and a controller for causing the optical system and processor to measure asymmetry in at least two of said targets which have different known components of positional offset between structures and smaller sub-structures within a layer on the substrate and calculate from the results of said asymmetry measurements a measurement of a performance parameter of the lithographic process for structures of said smaller size. Also disclosed are substrates provided with a plurality of novel metrology targets formed by a lithographic process.
Abstract:
A process of selecting a measurement location, the process including: obtaining pattern data describing a pattern to be applied to substrates in a patterning process; obtaining a process characteristic measured during or following processing of a substrate, the process characteristic characterizing the processing of the substrate; determining a simulated result of the patterning process based on the pattern data and the process characteristic; and selecting a measurement location for the substrate based on the simulated result.