Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error

Auguste Lam; Alexander Ypma; Maxime Gatefait; David Deckers; Arne Koopman; Richard van Haren; Jan Beltman

doi:10.1117/12.2085497

19 March 2015 Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error

Auguste Lam, Alexander Ypma, Maxime Gatefait, David Deckers, Arne Koopman, Richard van Haren, Jan Beltman

Author Affiliations +

Proceedings Volume 9424, Metrology, Inspection, and Process Control for Microlithography XXIX; 94241L (2015) https://doi.org/10.1117/12.2085497
Event: SPIE Advanced Lithography, 2015, San Jose, California, United States

Abstract

On-product overlay can be improved through the use of context data from the fab and the scanner. Continuous improvements in lithography and processing performance over the past years have resulted in consequent overlay performance improvement for critical layers. Identification of the remaining factors causing systematic disturbances and inefficiencies will further reduce overlay. By building a context database, mappings between context, fingerprints and alignment & overlay metrology can be learned through techniques from pattern recognition and data mining. We relate structure (‘patterns’) in the metrology data to relevant contextual factors. Once understood, these factors could be moved to the known effects (e.g. the presence of systematic fingerprints from reticle writing error or lens and reticle heating). Hence, we build up a knowledge base of known effects based on data. Outcomes from such an integral (‘holistic’) approach to lithography data analysis may be exploited in a model-based predictive overlay controller that combines feedback and feedforward control [1]. Hence, the available measurements from scanner, fab and metrology equipment are combined to reveal opportunities for further overlay improvement which would otherwise go unnoticed.

Citation Download Citation

Auguste Lam, Alexander Ypma, Maxime Gatefait, David Deckers, Arne Koopman, Richard van Haren, and Jan Beltman "Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error", Proc. SPIE 9424, Metrology, Inspection, and Process Control for Microlithography XXIX, 94241L (19 March 2015); https://doi.org/10.1117/12.2085497

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