PREDICTION AND METROLOGY OF STOCHASTIC PHOTORESIST THICKNESS DEFECTS

PREDICTION AND METROLOGY OF STOCHASTIC PHOTORESIST THICKNESS DEFECTS

A mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: BAI, Kunlun, ZHANG, Cao, BUROV, Anatoly, VUKKADALA, Pradeep, GRAVES, Trey John S, LI, Xiaohan, PARSEY, Guy, HIGGINS, Craig
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
APPARATUS SPECIALLY ADAPTED THEREFOR
CALCULATING
CINEMATOGRAPHY
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
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 mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in photoresist thickness for a semiconductor device. The Z-height may be calculated at a coordinate in the X-direction and Y-direction.