A holistic approach to model-based stochastic-aware computational lithography

ChangAn Wang; Yongfa Fan; Mu Feng; Qian Xie; Jazer Wang; Chris Kaplan; Michael Crouse; Xiaoyang Li; Stephen Hsu; Peigen Cao; Yi-Hsing Peng; Stephen Chang; Jun Ye; Youping Zhang; Bin Cheng; Ken Yang; Leiwu Zheng; Jen-Shiang Wang; Austin Peng; Li-Hao Yeh; Cuiping Zhang; Rafael Howell; Alexander Tan; Yiqiong Zhao; Jun Lang; Xiaolong Zhang

doi:10.1117/12.2658508

28 April 2023 A holistic approach to model-based stochastic-aware computational lithography

ChangAn Wang, Yongfa Fan, Mu Feng, Qian Xie, Jazer Wang, Chris Kaplan, Michael Crouse, Xiaoyang Li, Stephen Hsu, Peigen Cao, Yi-Hsing Peng, Stephen Chang, Jun Ye, Youping Zhang, Bin Cheng, Ken Yang, Leiwu Zheng, Jen-Shiang Wang, Austin Peng, Li-Hao Yeh, Cuiping Zhang, Rafael Howell, Alexander Tan, Yiqiong Zhao, Jun Lang, Xiaolong Zhang

Author Affiliations +

Proceedings Volume 12494, Optical and EUV Nanolithography XXXVI; 124940B (2023) https://doi.org/10.1117/12.2658508
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

With the adoption of extreme ultraviolet (EUV) lithography for high-volume production of advanced nodes, stochastic variability and resulting failures, both post litho and post etch, have drawn increasing attention. There is a strong need for accurate models for stochastic edge placement error (SEPE) with a direct link to the induced stochastic failure probability (FP). Additionally, to prevent stochastic failure from occurring on wafers, a holistic stochastic-aware computational lithography suite of products is needed, such as stochastic-aware mask source optimization (SMO), stochastic-aware optical proximity correction (OPC), stochastic-aware lithography manufacturability check (LMC), and stochastic-aware process optimization and characterization. In this paper, we will present a framework to model both SEPE and FP. This approach allows us to study the correlation between SEPE and FP systematically and paves the way to directly correlate SEPE and FP. Additionally, this paper will demonstrate that such a stochastic model can be used to optimize source and mask to significantly reduce SEPE, minimize FP, and improve stochastic-aware process window. The paper will also propose a flow to integrate the stochastic model in OPC to enhance the stochastic-aware process window and EUV manufacturability.

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ChangAn Wang, Yongfa Fan, Mu Feng, Qian Xie, Jazer Wang, Chris Kaplan, Michael Crouse, Xiaoyang Li, Stephen Hsu, Peigen Cao, Yi-Hsing Peng, Stephen Chang, Jun Ye, Youping Zhang, Bin Cheng, Ken Yang, Leiwu Zheng, Jen-Shiang Wang, Austin Peng, Li-Hao Yeh, Cuiping Zhang, Rafael Howell, Alexander Tan, Yiqiong Zhao, Jun Lang, and Xiaolong Zhang "A holistic approach to model-based stochastic-aware computational lithography", Proc. SPIE 12494, Optical and EUV Nanolithography XXXVI, 124940B (28 April 2023); https://doi.org/10.1117/12.2658508

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