Alignment performance monitoring for ASML systems

Woong-Jae Chung; Vlad Temchenko; Tarja Hauck; Sebastian Schmidt

doi:10.1117/12.640414

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15 March 2006 Alignment performance monitoring for ASML systems

Woong-Jae Chung, Vlad Temchenko, Tarja Hauck, Sebastian Schmidt

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Proceedings Volume 6155, Data Analysis and Modeling for Process Control III; 61550G (2006) https://doi.org/10.1117/12.640414
Event: SPIE 31st International Symposium on Advanced Lithography, 2006, San Jose, California, United States

Abstract

In today's semiconductor industry downscaling of the IC design puts a stringent requirement on pattern overlay control. Tighter overlay requirements lead to exceedingly higher rework rates, meaning additional costs to manufacturing. Better alignment control became a target of engineering efforts to decrease rework rate for high-end technologies. Overlay performance is influenced by known parameters such as "Shift, Scaling, Rotation, etc", and unknown parameters defined as "Process Induced Variation", which are difficult to control by means of a process automation system. In reality, this process-induced variation leads to a strong wafer to wafer, or lot to lot variation, which are not easy to detect in the mass-production environment which uses sampling overlay measurements for only several wafers in a lot. An engineering task of finding and correcting a root cause for Process Induced Variations of overlay performance will be greatly simplified if the unknown parameters could be tracked for each wafer. This paper introduces an alignment performance monitoring method based on analysis of automatically generated "AWE" files for ASML scanner systems. Because "AWE" files include alignment results for each aligned wafer, it is possible to use them for monitoring, controlling and correcting the causes of "process induced" overlay performance without requiring extra measurement time. Since "AWE" files include alignment information for different alignment marks, it is also possible to select and optimize the best alignment recipe for each alignment strategy. Several case studies provided in our paper will demonstrate how AWE file analysis can be used to assist engineer in interpreting pattern alignment data. Since implementing our alignment data monitoring method, we were able to achieve significant improvement of alignment and overlay performance without additional overlay measurement time. We also noticed that the rework rate coming from alignment went down and stabilized at quite satisfactory level.

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Woong-Jae Chung, Vlad Temchenko, Tarja Hauck, and Sebastian Schmidt "Alignment performance monitoring for ASML systems", Proc. SPIE 6155, Data Analysis and Modeling for Process Control III, 61550G (15 March 2006); https://doi.org/10.1117/12.640414

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