DEEP LEARNING FOR SEMANTIC SEGMENTATION OF PATTERN

• Main Authors: Middlebrooks, Scott Anderson, Kiers, Antoine Gaston Marie, Maslow, Mark John, KOOPMAN, Adrianus Cornelis Matheus
• Format: Patent
• Language: English
• Published: 13.10.2022
• Subjects: 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

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.