Asml Netherlands B.v.

Gn Resound A/s

Gn Hearing A/s

Alexander Ypma

Richard Johannes Franciscus Van Haren

Hakki Ergün Cekli

Scott Anderson Middlebrooks

Adrianus Cornelis Matheus Koopman

Weitian Kou

Irina Lyulina

Jochem Sebastiaan Wildenberg

Cédric Désiré Grouwstra

Marc Hauptmann

Jasper Menger

Michiel Kupers

David Han

Aalbert De Vries

David Frans Simon Deckers

Dimitra Gkorou

Erik Johannes Maria Wallerbos

Stefan Cornelis Theodorus Van Der Sanden

Willem Seine Christian Roelofs

David Deckers

Georgios Tsirogiannis

Erik Henri Adriaan Delvigne

Dag Sonntag

Lydia Marianna Vergaij-Huizer

Iryna Dovbush

Manuel Giollo

Everhardus Cornelis Mos

Chenxi Lin

Vahid Bastani

Robert Jan Van Wijk

Jelle Nije

Franciscus Godefridus Casper Bijnen

Boris Menchtchikov

Peter Ten Berge

Emil Peter Schmitt-Weaver

Michael James Lercel

Daan Maurits Slotboom

Leon Paul Van Dijk

Sarathi Roy

Paul Frank Luehrmann

Simon Hendrik Celine Van Gorp

Cyrus Emil Tabery

Simon Philip Spencer Hastings

Hyunwoo Yu

Ruben Alvarez Sanchez

Almer Jacob Van Den Berg

Tzu-Chao Chen

Thomas Leo Maria Hoogenboom

Pierre-Yves Jerome Yvan Guittet

Christiaan Theodoor De Ruiter

Maialen Larranaga

Faegheh Hasibi

Carlo Lancia

Frans Reinier Spiering

Min-Sub Han

Shih-Chin Liu

Wei Duan

Arie Jeffrey Den Boef

Patricius Aloysius Jacobus Tinnemans

Willem Bastiaan Kleijn

David Yuheng Zhao

Edo Maria Hulsebos

Xing Lan Liu

Seyed Iman Mossavat

Wolfgang Helmut Henke

Marc Jurian Kea

Jean-Philippe Xavier Van Damme

Victor Emanuel Calado

Loek Johannes Petrus Verhees

Arne Leijon

Joseph Renier Gerardus Maria Leenen

Juan Manuel Gonzalez Huesca

Eduardus Johannes Gerardus Boon

Reza Sahraeian

Wolfgang Henke

Pavel Izikson

Jens Staecker

Svante Sten Johan Stadler

Tjeerd Maarten Hein Dijkstra

Job Geurts

Bert Devries

Eleftherios Koulierakis

Anjan Prasad Gantapara

Dirk-Jan Kernkamp

Hakki Ergun Cekli

Bert De Vries

Joseph Renier Gerardus M Leenen

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 predicting a property associated with a product unit. The method may include: obtaining a plurality of data sets, wherein each of the plurality of data sets includes data associated with a spatial distribution of a parameter across the product unit; representing each of the plurality of data sets as a multidimensional object; obtaining a convolutional neural network model trained with previously obtained multidimensional objects and properties of previous product units; and applying the convolutional neural network model to the plurality of multidimensional objects representing the plurality of data sets, to predict the property associated with the product unit.

Apparatus and method for property joint interpolation and prediction

A method of tuning a prediction model relating to at least one particular configuration of a manufacturing device. The method includes obtaining a function including at least a first function of first prediction model parameters associated with the at least one particular configuration, and a second function of the first prediction model parameters and second prediction model parameters associated with configurations of the manufacturing device and/or related devices other than the at least one particular configuration. Values of the first prediction model parameters are obtained based on an optimization of the function, and a prediction model is tuned according to these values of the first prediction model parameters to obtain a tuned prediction mode.

Methods of modelling systems for performing predictive maintenance of systems, such as lithographic systems

A lithographic process is performed on a set of semiconductor substrates consisting of a plurality of substrates. As part of the process, the set of substrates is partitioned into a number of subsets. The partitioning may be based on a set of characteristics associated with a first layer on the substrates. A fingerprint of a performance parameter is then determined for at least one substrate of the set of substrates. Under some circumstances, the fingerprint is determined for one substrate of each subset of substrates. The fingerprint is associated with at least the first layer. A correction for the performance parameter associated with an application of a subsequent layer is then derived, the derivation being based on the determined fingerprint and the partitioning of the set of substrates.

Method and apparatus for optimization of lithographic process

Substrates to be processed are partitioned based on pre-processing data that is associated with substrates before a process step. The data is partitioned using a partition rule and the substrates are partitioned into subsets in accordance with subsets of the data obtained by the partitioning. Corrections are applied, specific to each subset. The partition rule is obtained using decision tree analysis on a training set of substrates. The decision tree analysis uses pre-processing data associated with the training substrates before they were processed, and post-processing data associated with the training substrates after being subject to the process step. The partition rule that defines the decision tree is selected from a plurality of partition rules based on a characteristic of subsets of the post-processing data. The associated corrections are obtained implicitly at the same time.

Method to label substrates based on process parameters

A method of determining a correction for a process parameter related to a lithographic process, wherein the lithographic process includes a plurality of runs during each one of which a pattern is applied to one or more substrates. The method of determining includes obtaining pre-exposure metrology data describing a property of a substrate; obtaining post-exposure metrology data comprising one or more measurements of the process parameter having been performed on one or more previously exposed substrates; assigning, based on the pre-exposure metrology data, a group membership status from one or more groups to the substrate; and determining the correction for the process parameter based on the group membership status and the post-exposure metrology data.

Methods of determining corrections for a patterning process, device manufacturing method, control system for a lithographic apparatus and lithographic apparatus

A method for determining an inspection strategy for at least one substrate, the method including: quantifying, using a prediction model, a compliance metric value for a compliance metric relating to a prediction of compliance with a quality requirement based on one or both of pre-processing data associated with the substrate and any available post-processing data associated with the at least one substrate; and deciding on an inspection strategy for the at least one substrate, based on the compliance metric value, an expected cost associated with the inspection strategy and at least one objective value describing an expected value of the inspection strategy in terms of at least one objective relating to the prediction model.

Method for determining an inspection strategy for a group of substrates in a semiconductor manufacturing process

A method and associated computer program for predicting an electrical characteristic of a substrate subject to a process. The method includes determining a sensitivity of the electrical characteristic to a process characteristic, based on analysis of electrical metrology data including electrical characteristic measurements from previously processed substrates and of process metrology data including measurements of at least one parameter related to the process characteristic measured from the previously processed substrates; obtaining process metrology data related to the substrate describing the at least one parameter; and predicting the electrical characteristic of the substrate based on the sensitivity and the process metrology data.

Method to predict yield of a device manufacturing process

A method of extracting a feature from a data set includes iteratively extracting a feature from a data set based on a visualization of a residual pattern within the data set, wherein the feature is distinct from a feature extracted in a previous iteration, and the visualization of the residual pattern uses the feature extracted in the previous iteration. Visualizing the data set using the feature extracted in the previous iteration may include showing residual patterns of attribute data that are relevant to target data. Visualizing the data set using the feature extracted in the previous iteration may involve adding cluster constraints to the data set, based on the feature extracted in the previous iteration. Additionally or alternatively, visualizing the data set using the feature extracted in the previous iteration may involve defining conditional probabilities conditioned on the feature extracted in the previous iteration.

Extracting a feature from a data set

A method of optimizing an apparatus for multi-stage processing of product units such as wafers, the method includes: receiving object data representing one or more parameters measured across the product units and associated with different stages of processing of the product units; and determining fingerprints of variation of the object data across the product units, the fingerprints being associated with different respective stages of processing of the product units. The fingerprints may be determined by decomposing the object data into components using principal component analysis for each different respective stage; analyzing commonality of the fingerprints through the different stages to produce commonality results; and optimizing an apparatus for processing product units based on the commonality results.

Optimizing an apparatus for multi-stage processing of product units

A method of grouping data associated with substrates undergoing a process step of a manufacturing process is disclosed. The method includes obtaining first data associated with substrates before being subject to the process step and obtaining a plurality of sets of second data associated with substrates after being subject to the process step, each set of second data being associated with a different value of a characteristic of the first data. A distance metric is determined which describes a measure of distance between the sets of second data, and the second data is grouped based on a property of the distance metric.

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Veldhoven, Netherlands

Alexander Ypma