The new OPC method for obtaining the stability of MBAF OPC

Song, Jookyoung; Choi, Jae-Seung; Park, Chanha; Yang, Hyunjo; Kang, Daekwon; Oh, Minsu; Park, Manjae; Moon, James; Ye, Jun; S, Baron

doi:10.1117/12.2257872

Cited by 1 publication

(1 citation statement)

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“…RB-SRAF is effective for simple designs but struggles with complex patterns, requiring frequent adjustments throughout development cycles [1]. On the other hand, MB-SRAF [2,3], including the more advanced Inverse Lithography Technology (ILT) [4,5], ensures high lithographic margin for intricate patterns. However, the computational complexity of ILT leads to excessive runtimes for full chip mask optimization.…”

Section: Introductionmentioning

confidence: 99%

Synthesizing ILT MB-SRAF using machine learning

Celepcikay,

Liao,

Huang

et al. 2024

DTCO and Computational Patterning III

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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 (MB-SRAF) 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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