Asml Netherlands B.v.

Asml Holding N.v.

Scott Anderson Middlebrooks

Adrianus Cornelis Matheus Koopman

Markus Gerardus Martinus Maria Van Kraaij

Maurits Van Der Schaar

Stefan Hunsche

Alexander Ypma

Richard Johannes Franciscus Van Haren

Willem Marie Julia Marcel Coene

Jochem Sebastiaan Wildenberg

Irina Lyulina

Jasper Menger

David Han

Antoine Gaston Marie Kiers

Alexander Straaijer

Everhardus Cornelis Mos

Hendrik Jan Hidde Smilde

Maxim Pisarenco

David Deckers

Niels Geypen

Andreas Fuchs

Sander Frederik Wuister

Hubertus Johannes Gertrudus Simons

Henricus Johannes Lambertus Megens

Mark John Maslow

Jan-Willem Gemmink

Panagiotis Pieter Bintevinos

Chrysostomos Batistakis

Willem Maria Julia Marcel Coene

Frank Arnoldus Johannes Maria Driessen

Arie Jeffrey Den Boef

Marcus Adrianus Van De Kerkhof

Patricius Aloysius Jacobus Tinnemans

Frank Staals

Patrick Warnaar

Jozef Maria Finders

Coen Adrianus Verschuren

Boris Menchtchikov

Mircea Dusa

Marinus Jochemsen

Richard Quintanilha

Christianus Gerardus Maria De Mol

Anton Bernhard Van Oosten

Leonardus Henricus Marie Verstappen

Alexander Viktorovych Padiy

Albertus Victor Gerardus Mangnus

Omer Abubaker Omer Adam

Mohammadreza Hajiahmadi

Grzegorz Grzela

Te-Sheng Wang

Brennan Peterson

Martyn John Coogans

Marc Johannes Noot

Lorenzo Tripodi

Farzad Farhadzadeh

Rene Andreas Maria Pluijms

Dongzi Wangli

In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in the multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Methods and apparatus for obtaining diagnostic information relating to an industrial process

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.

Method and apparatus for predicting substrate image

A method for determining an optimized weighting of an encoder and decoder network; the method comprising:

Aligning a distorted image

Disclosed is a method of determining a characteristic of interest relating to a structure on a substrate formed by a lithographic process, the method comprising: obtaining an input image of the structure; and using a trained neural network to determine the characteristic of interest from said input image. Also disclosed is a reticle comprising a target forming feature comprising more than two sub-features each having different sensitivities to a characteristic of interest when imaged onto a substrate to form a corresponding target structure on said substrate. Related methods and apparatuses are also described.

Metrology apparatus and method for determining a characteristic of one or more structures on a substrate

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.

Method of measuring variation, inspection system, computer program, and computer system

A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.

Systems and methods for predicting layer deformation

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.

Deep learning for semantic segmentation of pattern

A method and apparatus of detection, registration and quantification of an image is described. 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.

Method and apparatus for image analysis

A method of determining a measurement sequence for an inspection tool inspecting a structure generated by a lithographic process performed by a lithographic system is presented, the method including deriving a model for the lithographic process as performed by the lithographic system, the model including a relationship between a set of system variables describing the lithographic system and an output variable representing the structure resulting of the lithographic process, determining an observability of one or more system variables in the output variable, and determining the measurement sequence for the inspection tool, based on the observability.

Inspection tool, inspection method and computer program product

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.

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.

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Duizel, Netherlands

Scott Anderson Middlebrooks