Hotspot discovery and variability analysis for advanced EUV processes

Kaushik Sah; Zhijin Chen; Yao Zhang; Liming Zhang; Cao Zhang; Craig Higgins; Anatoly Burov; Guy Parsey; Pradeep Vukkadala; Roel Gronheid; Arpit Jain; Ramakanth Ramini; Ankur Agrawal; Garima Sharma; Andrew Cross; Syamashree Roy; Victor Blanco

doi:10.1117/12.3011178

9 April 2024 Hotspot discovery and variability analysis for advanced EUV processes

Kaushik Sah, Zhijin Chen, Yao Zhang, Liming Zhang, Cao Zhang, Craig Higgins, Anatoly Burov, Guy Parsey, Pradeep Vukkadala, Roel Gronheid, Arpit Jain, Ramakanth Ramini, Ankur Agrawal, Garima Sharma, Andrew Cross, Syamashree Roy, Victor Blanco

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553H (2024) https://doi.org/10.1117/12.3011178
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

To maintain lithographic pitch scaling, extreme ultraviolet (EUV) processes have been adopted in high-volume manufacturing (HVM) for today’s advanced logic and memory devices. Among various defect sources, stochastic patterning defects are one of the most important yield detractors for EUV processes. In this work, we will limit our scope to patterning defects arising out of lithography. In the past, it has been shown that the patterning defect process window is often limited by stochastic hotspots. These hotspots have very low failure probabilities in a well-optimized process, and hence their detection necessitates large area sensitive defect inspection, such as with a broadband plasma (BBP) optical defect inspection system. It has also been shown that systematic issues in design can be exacerbated by stochastic variations. Hence, it is critical to discover these hotspots and study their variability with massive SEM metrology. Such analyses can uncover systematic trends, which can then be corrected and monitored. In this work, we discover hotspots using broadband plasma (BBP) optical inspection and study their variability using KLA’s aiSIGHT™ pattern-centric defect and metrology software solution for automatic defect classification and SEM metrology measurements. We also demonstrate the need for fast and rigorous 3D probabilistic stochastic defect detection on design as a continuation of this work.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Kaushik Sah, Zhijin Chen, Yao Zhang, Liming Zhang, Cao Zhang, Craig Higgins, Anatoly Burov, Guy Parsey, Pradeep Vukkadala, Roel Gronheid, Arpit Jain, Ramakanth Ramini, Ankur Agrawal, Garima Sharma, Andrew Cross, Syamashree Roy, and Victor Blanco "Hotspot discovery and variability analysis for advanced EUV processes", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553H (9 April 2024); https://doi.org/10.1117/12.3011178

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