The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 31, 2026
Filed:
Jun. 14, 2021
Asml Netherlands B.v., Veldhoven, NL;
Satej Subhash Khedekar, Veldhoven, NL;
Henricus Jozef Castelijns, Hapert, NL;
Anjan Prasad Gantapara, Veldhoven, NL;
Stephen Henry Bond, Eijsden, NL;
Seyed Iman Mossavat, Waalre, NL;
Alexander Ypma, Veldhoven, NL;
Gerald Dicker, Sint-Oedenrode, NL;
Ewout Klaas Steinmeier, Waalre, NL;
Chaoqun Guo, Shenzhen, CN;
Chenxi Lin, Newark, CA (US);
Hongwei Chen, Shenzhen, CN;
Zhaoze Li, Shenzhen, CN;
Youping Zhang, Cupertino, CA (US);
Yi Zou, Foster City, CA (US);
Koos Van Berkel, Waalre, NL;
Joost Johan Bolder, Waalre, NL;
Arnaud Hubaux, Erpent, NL;
Andriy Vasyliovich Hlod, Eindhoven, NL;
Juan Manuel Gonzalez Huesca, Eindhoven, NL;
Frans Bernard Aarden, Eindhoven, NL;
ASML NETHERLANDS B.V., Veldhoven, NL;
Abstract
Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input includes one or more parameters used in the patterning process. The control output is generated with a trained machine learning model based on the control input. The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data. The training data includes 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.