Mr. Szu-Ping Chen Profile

Szu-Ping Chen

at Nanya Technology Corp.

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Proceedings Article | 10 April 2024 Presentation + Paper

Synthesizing ILT MB-SRAF using machine learning

Ferhat Turker Celepcikay, Chun-Cheng Liao, Teng-Yen Huang, Szu-Ping Chen, Chi Neng Lin, Alan Zhu, Hung Yu Lin

Proceedings Volume 12954, 129540H (2024) https://doi.org/10.1117/12.3010902

KEYWORDS: SRAF, Machine learning, Resolution enhancement technologies, Lithography, Image quality, Optical proximity correction

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For advanced technology nodes, it’s critical to utilize resolution enhancement technique (RET) methods to improve pattern fidelity and wafer yield. Conventional techniques including rule-based SRAF (RB-SRAF) and model-based SRAF (MBSRAF) methods have been widely adopted to increase the manufacturing process window. ILT delivers superior imaging performance compared to both RB-SRAF and MB-SRAF methods, at the expense of slower performance and more inconsistency issue. Recent advancement of machine learning techniques opens up new gateways for more RET enhancements by overcoming these challenges, thus providing a pathway to extend ILT solution to full chip design. In this paper, we developed an end-to-end flow that seamlessly incorporated model training and application for full chip ILT MBSRAF generation and optimization via POLY-GAN, a new Generative Adversarial network (GAN) geared for fast, in-context and accurate ILT MB-SRAF synthesis. An image based deep learning architecture similar to pix2pix conditional GAN was utilized in our study. In this paper, we demonstrate that ML based full chip ILT MBSRAF generation yields superior process window compared to rule based SRAF generation, while maintaining comparable run-time performance.

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