Asml Netherlands B.v.

Asml Holding N.v.

Imec Vzw

Maurits Van Der Schaar

Arie Jeffrey Den Boef

Everhardus Cornelis Mos

Richard Johannes Franciscus Van Haren

Kaustuve Bhattacharyya

Patrick Warnaar

Hendrik Jan Hidde Smilde

Andreas Fuchs

Antoine Gaston Marie Kiers

Youping Zhang

Mircea Dusa

Michael Kubis

Marcus Adrianus Van De Kerkhof

Scott Anderson Middlebrooks

Murat Bozkurt

Henricus Petrus Maria Pellemans

Markus Gerardus Martinus Maria Van Kraaij

Paul Frank Luehrmann

Arno Jan Bleeker

Hugo Augustinus Joseph Cramer

Paul Christiaan Hinnen

Henricus Wilhelmus Maria Van Buel

Youri Johannes Laurentius Maria Van Dommelen

Omer Abubaker Omer Adam

Martin Jacobus Johan Jak

Xing Lan Liu

Stefan Carolus Jacobus Antonius Keij

Olger Victor Zwier

Cedric Desire Grouwstra

Christophe David Fouquet

Johannes Marcus Maria Beltman

Martyn John Coogans

Alexander Straaijer

Hubertus Johannes Gertrudus Simons

Jacobus Burghoorn

Guangqing Chen

Niels Geypen

Patricius Aloysius Jacobus Tinnemans

Cheng-Qun Gui

Te-Chih Huang

Irwan Dani Setija

Leonardus Henricus Marie Verstappen

Vasco Tomas Tenner

Mohammadreza Hajiahmadi

Victor Emanuel Calado

Chi-Hsiang Fan

Joost Jeroen Ottens

Frank Staals

Nitesh Pandey

Hans Van Der Laan

Thomas Theeuwes

Armand Eugene Albert Koolen

Anagnostis Tsiatmas

Adriaan Johan Van Leest

Karel Diederick Van Der Mast

Hakki Ergün Cekli

Jochem Sebastiaan Wildenberg

Martin Ebert

Elliott Gerard McNamara

Alok Verma

Jen-Shiang Wang

Cédric Désiré Grouwstra

Henricus Johannes Lambertus Megens

Peter Hanzen Wardenier

Sami Musa

Erik Johannes Maria Wallerbos

Zili Zhou

Stefan Cornelis Theodorus Van Der Sanden

Bart Swinnen

Grzegorz Grzela

Rene Monshouwer

Henry Megens

Sicco Ian Schets

Marc Jurian Kea

Gonzalo Roberto Sanguinetti

Pieter Willem Herman De Jager

Wim Tjibbo Tel

Justin Lloyd Kreuzer

Harry Sewell

Simon Gijsbert Josephus Mathijssen

Stefan Hunsche

Franciscus Godefridus Casper Bijnen

Sebastianus Adrianus Goorden

Willem Marie Julia Marcel Coene

Roy Werkman

Maxim Pisarenco

Reiner Maria Jungblut

Maikel Robert Goosen

Leon Paul Van Dijk

Seyed Iman Mossavat

Jin Lian

Lukasz Jerzy Macht

Paul Jacques Van Wijnen

Willem Seine Christian Roelofs

Jeroen Huijbregtse

Jeroen Cottaar

Sergey Tarabrin

Erik Johan Koop

Alberto Da Costa Assafrao

Peter Clement Paul Vanoppen

A method of metrology target design is described. The method includes determining a sensitivity of a parameter of a metrology target design to a perturbation of a process parameter for forming, or measuring the formation of, the metrology target, and determining a robustness of the metrology target design based on the sum of the sensitivity multiplied by the perturbation of at least one of the process parameters.

Method and apparatus for design of a metrology target

A diffraction measurement target that has at least a first sub-target and at least a second sub-target, and wherein (1) the first and second sub-targets each include a pair of periodic structures and the first sub-target has a different design than the second sub-target, the different design including the first sub-target periodic structures having a different pitch, feature width, space width, and/or segmentation than the second sub-target periodic structure or (2) the first and second sub-targets respectively include a first and second periodic structure in a first layer, and a third periodic structure is located at least partly underneath the first periodic structure in a second layer under the first layer and there being no periodic structure underneath the second periodic structure in the second layer, and a fourth periodic structure is located at least partly underneath the second periodic structure in a third layer under the second layer.

Metrology method, target and substrate

A method for applying a deposition model in a semiconductor manufacturing process. The method includes predicting a deposition profile of a substrate using the deposition model; and using the predicted deposition profile to enhance a metrology target design. The deposition model can be calibrated using experimental cross-section profile information from a layer of a physical substrate. In some embodiments, the deposition model is a machine-learning model, and calibrating the deposition model includes training the machine-learning model. The metrology target design may include an alignment metrology target design or an overlay metrology target design, for example.

Method for applying a deposition model in a semiconductor manufacturing process

Disclosed is a substrate and associated patterning device. The substrate comprises at least one target arrangement suitable for metrology of a lithographic process, the target arrangement comprising at least one pair of similar target regions which are arranged such that the target arrangement is, or at least the target regions for measurement in a single direction together are, centrosymmetric. A metrology method is also disclosed for measuring the substrate. A metrology method is also disclosed comprising which comprises measuring such a target arrangement and determining a value for a parameter of interest from the scattered radiation, while correcting for distortion of the metrology apparatus used.

Substrate comprising a target arrangement, and associated at least one patterning device, lithographic method and metrology method

Disclosed is target arrangement comprising a first target region having at least a first pitch and at least a second pitch a second target region having at least a third pitch, wherein a portion of the first target region having a second pitch overlaps with a portion of the second target region.

Target for measuring a parameter of a lithographic process

A method including performing a simulation to evaluate a plurality of metrology targets and/or a plurality of metrology recipes used to measure a metrology target, identifying one or more metrology targets and/or metrology recipes from the evaluated plurality of metrology targets and/or metrology recipes, receiving measurement data of the one or more identified metrology targets and/or metrology recipes, and using the measurement data to tune a metrology target parameter or metrology recipe parameter.

Method and apparatus for inspection and metrology

Disclosed is a method comprising measuring radiation reflected from a metrology target and decomposing the measured radiation in components, for example Fourier components or spatial components. Further, there is disclosed a recipe selection method which provides an algorithm to select a parameter of the metrology apparatus based on re-calculated dependencies of 5 the measured radiation based on single components.

Metrology method and associated computer product

Disclosed is a method for obtaining a computationally determined interference electric field describing scattering of radiation by a pair of structures comprising a first structure and a second structure on a substrate. The method comprises determining a first electric field relating to first radiation scattered by the first structure; determining a second electric field relating to second radiation scattered by the second structure; and computationally determining the interference of the first electric field and second electric field, to obtain a computationally determined interference electric field.

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

A target for determining a performance parameter of a lithographic process, the target comprising a first sub-target formed by at least two overlapping gratings, wherein the underlying grating of the first sub-target has a first pitch and the top lying grating of the first sub-target has a second pitch, at least a second sub-target formed by at least two overlapping gratings, wherein the underlying grating of the second sub-target has a third pitch and the top lying grating of the second sub-target has a fourth pitch.

A patterning device for patterning product structures onto a substrate and an associated substrate patterned using such a patterning device. The patterning device includes target patterning elements for patterning at least one target from which a parameter of interest can be inferred. The patterning device includes product patterning elements for patterning the product structures. The target patterning elements and product patterning elements are configured such that the at least one target has at least one boundary which is neither parallel nor perpendicular with respect to the product structures on the substrate.

Metrology targets

Multilayered product structures are formed on substrates by a combination of patterning steps, physical processing steps and chemical processing steps. An inspection apparatus illuminates a plurality of target structures and captures pupil images representing the angular distribution of radiation scattered by each target structure. The target structures have the same design but are formed at different locations on a substrate and/or on different substrates. Based on a comparison of the images the inspection apparatus infers the presence of process-induced stack variations between the different locations. In one application, the inspection apparatus separately measures overlay performance of the manufacturing process based on dark-field images, combined with previously determined calibration information. The calibration is adjusted for each target, depending on the stack variations inferred from the pupil images.

Methods and apparatus for monitoring a manufacturing process, inspection apparatus, lithographic system, device manufacturing method

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.

Method and apparatus for angular-resolved spectroscopic lithography characterization

A method for improving the yield of a lithographic process, the method including: determining a parameter fingerprint of a performance parameter across a substrate, the parameter fingerprint including information relating to uncertainty in the performance parameter; determining a process window fingerprint of the performance parameter across the substrate, the process window being associated with an allowable range of the performance parameter; and determining a probability metric associated with the probability of the performance parameter being outside an allowable range. Optionally a correction to the lithographic process is determined based on the probability metric.

Method for optimization of a lithographic process

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Eindhoven, Netherlands

Maurits Van Der Schaar