Physical dose modeling and throughput optimization in EUV computational lithography

Austin Peng; Christopher Kaplan; Jeff Lu; Michael Crouse; Zuanyi Li; Xiaobo Xie; David Rio; Achim Woessner; Alexander Tan; Cuiping Zhang; Xiaoyang Li; Dezheng Sun; Stephen Hsu; Rafael Howell; Joerg Zimmermann

doi:10.1117/12.2657454

28 April 2023 Physical dose modeling and throughput optimization in EUV computational lithography

Austin Peng, Christopher Kaplan, Jeff Lu, Michael Crouse, Zuanyi Li, Xiaobo Xie, David Rio, Achim Woessner, Alexander Tan, Cuiping Zhang, Xiaoyang Li, Dezheng Sun, Stephen Hsu, Rafael Howell, Joerg Zimmermann

Author Affiliations +

Proceedings Volume 12494, Optical and EUV Nanolithography XXXVI; 124940C (2023) https://doi.org/10.1117/12.2657454
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Despite being crucial in an optical lithography process, “dose” has remained a relative concept in the computational lithography regime. It usually takes the form of a percentage deviation from a pre-identified “nominal condition” under the same illumination shape. Dose comparison between different illumination shapes has never been rigorously defined and modeled in numerical simulation to date. On the other hand, the exposure-limited nature of EUV lithography throughput demands the * illumination shape being optimized with the physical dose impact consciously taken into consideration. When the projection pupil is significantly obscured (as in the ASML EXE high NA scanner series), the lack of a proper physical dose constraint may lead to suboptimal energy utilization during exposure. In this paper, we demonstrate a method to accurately model the physical dose in an optical lithography process. The resultant dose concept remains meaningful in the context of a changing illumination pupil, which enables co-optimization of imaging quality and a throughput metric during the Source-Mask Optimization (SMO) phase, known as the Dose-Aware SMO. With a few realistic test cases we demonstrate the capability of Dose-Aware SMO in terms of improving EUV throughput via reducing the effective exposure time, in both regular and obscured projection systems. The physical dose modeling capability in computational lithography not only addresses those immediate challenges emergent from EUV throughput, but also opens the gate towards a broad class of exciting topics that are built upon physical dose, such as optical stochastic phenomena and so on.

Conference Presentation

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Austin Peng, Christopher Kaplan, Jeff Lu, Michael Crouse, Zuanyi Li, Xiaobo Xie, David Rio, Achim Woessner, Alexander Tan, Cuiping Zhang, Xiaoyang Li, Dezheng Sun, Stephen Hsu, Rafael Howell, and Joerg Zimmermann "Physical dose modeling and throughput optimization in EUV computational lithography", Proc. SPIE 12494, Optical and EUV Nanolithography XXXVI, 124940C (28 April 2023); https://doi.org/10.1117/12.2657454

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