Detecting outliers and anomalies for OCD metrology machine learning

A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are...

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Bibliographische Detailangaben
Hauptverfasser:	Kim, Yongha, Rothstein, Eitan A, Broitman, Ariel, Bringoltz, Barak, Krasnykov, Olga, Rubinovich, Ilya
Format:	Patent
Sprache:	eng
Schlagworte:	APPARATUS SPECIALLY ADAPTED THEREFOR CINEMATOGRAPHY ELECTROGRAPHY HOLOGRAPHY MATERIALS THEREFOR MEASURING MEASURING ANGLES MEASURING AREAS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS ORIGINALS THEREFOR PHOTOGRAPHY PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES,e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTORDEVICES PHYSICS TESTING
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Beschreibung
Zusammenfassung:	A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are by calculated and corresponding outlier thresholds are applied whether a given pair is an outlier pair. The OCD MIL model is then trained with the training data less the outlier pairs.