Asml Netherlands B.v.

Wim Tjibbo Tel

Frank Staals

Marinus Jochemsen

Mark John Maslow

Stefan Hunsche

Emil Peter Schmitt-Weaver

Yichen Zhang

Abraham Slachter

Everhardus Cornelis Mos

Reiner Maria Jungblut

Hermanus Adrianus Dillen

Koenraad Van Ingen Schenau

Roy Anunciado

Paul Christiaan Hinnen

Patrick Warnaar

Hans Van Der Laan

Thomas Theeuwes

Bearrach Moest

Sarathi Roy

Bart Peter Bert Segers

Johannes Jacobus Matheus Baselmans

Kaustuve Bhattacharyya

Andre Bernardus Jeunink

Antoine Gaston Marie Kiers

Xing Lan Liu

Daan Maurits Slotboom

Anton Bernhard Van Oosten

Simon Hendrik Celine Van Gorp

Marc Jurian Kea

M'hamed Akhssay

Alexander Hendrikus Martinus Van Der Hoff

Simon De Groot

Hyunwoo Yu

Franciscus Antonius Chrysogonus Marie Commissaris

Erik Roelof Loopstra

Joeri Lof

Richard Johannes Franciscus Van Haren

Kars Zeger Troost

Leon Martin Levasier

Roy Werkman

Paul Frank Luehrmann

Weitian Kou

Stefan Geerte Kruijswijk

Johannes Wilhelmus De Klerk

Cassandra May Owen

Paul Jacques Van Wijnen

Theodore Allen Paxton

Todd J Davis

Wolfgang Helmut Henke

Martin Jules Marie-Emile De Nivelle

Todd David Hiar

Brennan Peterson

Gijsbert Rispens

Remco Marcel Van Dijk

Johannes Catharinus Hubertus Mulkens

Joost Jeroen Ottens

Maurits Van Der Schaar

Patricius Aloysius Jacobus Tinnemans

Arno Jan Bleeker

Hugo Augustinus Joseph Cramer

Jozef Maria Finders

Youping Zhang

Yi Zou

Adriaan Johan Van Leest

Hakki Ergün Cekli

Tammo Uitterdijk

Jochem Sebastiaan Wildenberg

Maxim Pisarenco

Laurentius Cornelius De Winter

Thomas Ingolf Wallow

Cédric Désiré Grouwstra

Rene Theodorus Petrus Compen

Peter Hanzen Wardenier

Yana Cheng

Laurentius Catrinus Jorritsma

Alexander Viktorovych Padiy

Leon Paul Van Dijk

Aschwin Lodewijk Hendricus Johannes Van Meer

Chenxi Lin

Stefan Cornelis Theodorus Van Der Sanden

Rene Marinus Gerardus Johan Queens

Wouter Onno Pril

Willem Seine Christian Roelofs

Te-Sheng Wang

Grzegorz Grzela

Simon Philip Spencer Hastings

Peter David Engblom

Cyrus Emil Tabery

Sven Gunnar Krister Magnusson

Maxime Philippe Frederic Genin

Johannes Marcus Maria Beltman

Jacob Willem Vink

Robert Kazinczi

Manouk Rijpstra

Marleen Kooiman

Tzu-Chao Chen

Ruben Cornelis Maas

Orion Jonathan Pierre Mouraille

Pieter Gerardus Jacobus Smorenberg

Ralph Timotheus Huijgen

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.

Method for determining defectiveness of pattern based on after development image

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.

Method in the manufacturing process of a device, a non-transitory computer-readable medium and a system configured to perform the method

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.

Selection of measurement locations for patterning processes

A method for determining a measurement recipe describing one or more measurement settings for measuring a parameter of interest from a substrate subject to an etch induced parameter error, the etch induced parameter error affecting measurement of the parameter of interest in a recipe dependent manner. The method include obtaining parameter of interest set-up data relating to measurements of at least one set-up substrate on which the parameter of interest has various first induced set values and etch induced parameter set-up data relating to measurements of at least one set-up substrate on which the etch induced parameter has various second induced set values. The recipe is determined so as to minimize the effect of the etch induced parameter on measurement of the parameter of interest.

Method for determining a measurement recipe and associated apparatuses

A method including: obtaining an image of at least part of a substrate, wherein the image includes at least one feature manufactured on the substrate by a manufacturing process including a lithographic process and one or more further processes; determining one or more image-related metrics in dependence on a contour determined from the image, wherein one of the one or more image-related metrics is an edge placement error, EPE, of the at least one feature; and determining one or more control parameters of the lithographic process and/or the one or more further processes in dependence on the edge placement error, wherein at least one control parameter is determined so as to minimize the edge placement error of the at least one feature.

Method of determining control parameters of a device manufacturing process

A method, involving determining a first distribution of a first parameter associated with an error or residual in performing a device manufacturing process; determining a second distribution of a second parameter associated with an error or residual in performing the device manufacturing process; and determining a distribution of a parameter of interest associated with the device manufacturing process using a function operating on the first and second distributions. The function may include a correlation.

Computational metrology

A method of determining a position of a product feature on a substrate, the method includes: obtaining a plurality of position measurements of one or more product features on a substrate, wherein the measurements are referenced to either a positioning system used in displacing the substrate in between measurements or a plane parallel to the surface of the substrate; and determining a distortion component of the substrate based on the position measurements.

Method of wafer alignment using at resolution metrology on product features

A method for generating metrology sampling scheme for a patterning process, the method including: obtaining a parameter map of a parameter of a patterning process for a substrate; decomposing the parameter map to generate a fingerprint specific to an apparatus of the patterning process and/or a combination of apparatuses of the patterning process; and based on the fingerprint, generating a metrology sampling scheme for a subsequent substrate at the apparatus of the patterning process and/or the combination of apparatuses of the patterning process, wherein the sampling scheme is configured to distribute sampling points on the subsequent substrate so as to improve a metrology sampling density.

Computational metrology based sampling scheme

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.

Method of manufacturing devices

A method for calibrating a scanning charged particle microscope, such as a scanning electron microscope (SEM), is provided. The method includes dividing a wafer into a plurality of regions; preparing, on each of the plurality of regions, a pattern including a first periodic structure interleaved with a second periodic structure, the first and second periodic structures having an induced offset; determining an actual pitch the first and second periodic structures and thereby determining actual induced offset on each of the plurality of regions; selecting a plurality of regions from among the plurality of regions; measuring, by the SEM, a pitch of first and second periodic structures on each of the plurality of regions; and performing linearity calibration on the SEM based on the determining and the measuring.

Method for calibrating a scanning charged particle microscope

A method of determining a characteristic of one or more processes for manufacturing features on a substrate, the method including: obtaining image data of a plurality of features on a least part of at least one region on a substrate; using the image data to obtain measured data of one or more dimensions of each of at least some of the plurality of features; determining a statistical parameter that is dependent on the variation of the measured data of one or more dimensions of each of at least some of the plurality of features; determining a probability of defective manufacture of features in dependence on a determined number of defective features in the image data; and determining the characteristic of the one or more processes to have the probability of defective manufacture of features and the statistical parameter.

Method for characterizing a manufacturing process of semiconductor devices

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.

Process window based on defect probability

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.

A method involving determining a contribution that one or more process apparatuses make to a characteristic of a substrate after the substrate has been processed according to a patterning process by the one or more process apparatuses by removing from values of the characteristic of the substrate a contribution of a lithography apparatus to the characteristic and a contribution of one or more pre-lithography process apparatuses to the characteristic.

Method and system to monitor a process apparatus

A method for characterizing post-processing data in terms of individual contributions from processing stations, the post-processing data relating to a manufacturing process for manufacturing integrated circuits on a plurality of substrates using a corresponding processing apparatus for each of a plurality of process steps, at least some of the processing apparatuses each including a plurality of the processing stations, and wherein the combination of processing stations used to process each substrate defines a process thread for the substrate; the method including: obtaining post-processing data associated with processing of the plurality of substrates in a cyclic sequence of processing threads; and determining an individual contribution of a particular processing station by comparing a subset of the post-processing data corresponding to substrates having shared process sub-threads, wherein a process sub-thread describes the process steps of each process thread other than the process step to which the particular processing station corresponds.

Method to characterize post-processing data in terms of individual contributions from processing stations

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Helmond, Netherlands

Wim Tjibbo Tel