Asml Netherlands B.v.

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Alexander Ypma

Hakki Ergün Cekli

Chenxi Lin

Vahid Bastani

Boris Menchtchikov

Peter Ten Berge

Michael James Lercel

Simon Hendrik Celine Van Gorp

Cyrus Emil Tabery

Simon Philip Spencer Hastings

Ruben Alvarez Sanchez

Pierre-Yves Jerome Yvan Guittet

Christiaan Theodoor De Ruiter

Shih-Chin Liu

Wei Duan

Everhardus Cornelis Mos

Georgios Tsirogiannis

Dimitra Gkorou

Robert Jan Van Wijk

Jelle Nije

Reza Sahraeian

A method of determining a contribution of a process feature to the performance of a process of patterning substrates. The method may include obtaining a first model trained on first process data and first performance data. One or more substrates may be identified based on a quality of prediction of the first model when applied to process data associated with the one or more substrates. A second model may be trained on second process data and second performance data associated with the identified one or more substrates. The second model may be used to determine the contribution of a process feature of the second process data to the second performance data associated with the one or more substrates.

Method and apparatus for determining feature contribution to performance

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 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 maintaining a set of fingerprints representing variation of one or more process parameters across wafers subjected to a device manufacturing method, the method including: receiving measurement data of one or more parameters measured on wafers; updating the set of fingerprints based on an expected evolution of the one or more process parameters; and evaluation of the updated set of fingerprints based on decomposition of the received measurement data in terms of the updated set of fingerprints. Each fingerprint may have a stored likelihood of occurrence, and the decomposition may involve: estimating, based the received measurement data, likelihoods of occurrence of the set of fingerprints in the received measurement data; and updating the stored likelihoods of occurrence based on the estimated likelihoods.

Maintaining a set of process fingerprints

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Eindhoven, Netherlands

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