Overlay improvements using a real time machine learning algorithm

Emil Schmitt-Weaver; Michael Kubis; Wolfgang Henke; Daan Slotboom; Tom Hoogenboom; Jan Mulkens; Martyn Coogans; Peter ten Berge; Dick Verkleij; Frank van de Mast

doi:10.1117/12.2046914

2 April 2014 Overlay improvements using a real time machine learning algorithm

Emil Schmitt-Weaver, Michael Kubis, Wolfgang Henke, Daan Slotboom, Tom Hoogenboom, Jan Mulkens, Martyn Coogans, Peter ten Berge, Dick Verkleij, Frank van de Mast

Author Affiliations +

Proceedings Volume 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII; 90501S (2014) https://doi.org/10.1117/12.2046914
Event: SPIE Advanced Lithography, 2014, San Jose, California, United States

Abstract

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system’s sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

Citation Download Citation

Emil Schmitt-Weaver, Michael Kubis, Wolfgang Henke, Daan Slotboom, Tom Hoogenboom, Jan Mulkens, Martyn Coogans, Peter ten Berge, Dick Verkleij, and Frank van de Mast "Overlay improvements using a real time machine learning algorithm", Proc. SPIE 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII, 90501S (2 April 2014); https://doi.org/10.1117/12.2046914

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