DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING

A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-sp...

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Bibliographische Detailangaben
Hauptverfasser:	ERICKSON, Blake, KUTNEY, Michael, ZHU, Zhaozhao, UMMETHALA, Upendra, KUMAR, Prashanth, TINDEL, Steven Trey
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	APPARATUS SPECIALLY ADAPTED THEREFOR CALCULATING CINEMATOGRAPHY COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING ELECTROGRAPHY HOLOGRAPHY MATERIALS THEREFOR ORIGINALS THEREFOR PHOTOGRAPHY PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES,e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTORDEVICES PHYSICS
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Beschreibung
Zusammenfassung:	A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-spectral data associated with a surface of a prior substrate previously processed at the manufacturing system is generated. A first target output for the first training input is generated. The first target output includes historical metrology measurements associated with the prior substrate previously processed at the manufacturing system. Data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including a first target output.