公开(公告)号：US09971251B2

公开(公告)日：2018-05-15

申请号：US14905611

申请日：2014-08-06

Applicant: ASML Netherlands B.V.

Inventor： Emil Peter Schmitt-Weaver ,  Wolfgang Henke ,  Thomas Leo Maria Hoogenboom ,  Pavel Izikson ,  Paul Frank Luehrmann ,  Daan Maurits Slotboom ,  Jens Staecker ,  Alexander Ypma

IPC: G03B27/42 ,  G03F7/20

CPC classification number: G03F7/70516 ,  G03F7/70491 ,  G03F7/70525 ,  G03F7/70625 ,  G03F7/70633

Abstract: A lithography system configured to apply a pattern to a substrate, the system including a lithography apparatus configured to expose a layer of the substrate according to the pattern, and a machine learning controller configured to control the lithography system to optimize a property of the pattern, the machine learning controller configured to be trained on the basis of a property measured by a metrology unit configured to measure the property of the exposed pattern in the layer and/or a property associated with exposing the pattern onto the substrate, and to correct lithography system drift by adjusting one or more selected from: the lithography apparatus, a track unit configured to apply the layer on the substrate for lithographic exposure, and/or a control unit configured to control an automatic substrate flow among the track unit, the lithography apparatus, and the metrology unit.

公开(公告)号：US11099486B2

公开(公告)日：2021-08-24

申请号：US16477619

申请日：2017-12-13

Applicant: ASML NETHERLANDS B.V.

Inventor： Alexander Ypma ,  Dimitra Gkorou ,  Georgios Tsirogiannis ,  Thomas Leo Maria Hoogenboom ,  Richard Johannes Franciscus Van Haren

IPC: G03F7/20 ,  G05B19/418 ,  H01L21/66

Abstract: A technique to generate predicted data for control or monitoring of a production process to improve a parameter of interest. Context data associated with operation of the production process is obtained. Metrology/testing is performed on the product of the production process, thereby obtaining performance data. A context-to-performance model is provided to generate predicted performance data based on labeling of the context data with performance data. This is an instance of semi-supervised learning. The context-to-performance model may include the learner that performs semi-supervised labeling. The context-to-performance model is modified using prediction information related to quality of the context data and/or performance data. Prediction information may include relevance information relating to relevance of the obtained context data and/or obtained performance data to the parameter of interest. The prediction information may include model uncertainty information relating to uncertainty of the predicted performance data.