DEEP LEARNING FOR SEMANTIC SEGMENTATION OF PATTERN

A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data comprising an input image of at least a part of a substrate having a plurality of features and a truth image, (ii) a set of classes, each class corresponding to a feature of the plura...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Middlebrooks, Scott Anderson, Kiers, Antoine Gaston Marie, Maslow, Mark John, KOOPMAN, Adrianus Cornelis Matheus
Format:	Patent
Sprache:	eng
Schlagworte:	APPARATUS SPECIALLY ADAPTED THEREFOR CALCULATING CINEMATOGRAPHY COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTROGRAPHY HOLOGRAPHY IMAGE DATA PROCESSING OR GENERATION, IN GENERAL MATERIALS THEREFOR ORIGINALS THEREFOR PHOTOGRAPHY PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES,e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTORDEVICES PHYSICS
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	A method for training a deep learning model of a patterning process. The method includes obtaining (i) training data comprising an input image of at least a part of a substrate having a plurality of features and a truth image, (ii) a set of classes, each class corresponding to a feature of the plurality of features of the substrate within the input image, and (iii) a deep learning model configured to receive the training data and the set of classes, generating a predicted image, by modeling and/or simulation of the deep learning model using the input image, assigning a class of the set of classes to a feature within the predicted image based on matching of the feature with a corresponding feature within the truth image, and generating, by modeling and/or simulation, a trained deep learning model by iteratively assigning weights using a loss function.