An efficient inverse lithography technology method based on detail-enhanced Pix2Pix network

Zuoxian Chen; Zheng Shi

doi:10.1117/12.3025959

1 April 2024 An efficient inverse lithography technology method based on detail-enhanced Pix2Pix network

Zuoxian Chen, Zheng Shi

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810D (2024) https://doi.org/10.1117/12.3025959
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

Abstract

Mask optimization has arisen as a vital challenge in the flow of VLSI manufacturing, primarily because the critical dimension of integrated circuits stands much smaller than the light source's wavelength. Inverse lithography technology (ILT), a notable resolution enhancement technology, is known for its efficacy in enhancing mask printability. However, its extensive computational complexity has obstructed its broad-spectrum adoption. In our paper, we introduce a detail-enhanced Pix2Pix network, founded on GAN principles, to speed up the ILT process. This network has the capacity to generate quasi-optimal masks from given target layouts, thereby reducing the amount of traditional ILT steps required to produce high-quality masks. Our experiments on the ICCAD 2013 benchmarks demonstrate that, in comparison to the latest cGAN-based method, the L2 error, PVB, and runtime in our work have seen reductions of 7.2%, 5.9%, and 18.4% respectively. Thus, our approach not only expedites the ILT process but also guarantees enhanced printability.

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

Citation Download Citation

Zuoxian Chen and Zheng Shi "An efficient inverse lithography technology method based on detail-enhanced Pix2Pix network", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810D (1 April 2024); https://doi.org/10.1117/12.3025959

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KEYWORDS

Convolution

Lithography

Optical proximity correction

Education and training

Gallium nitride

Feature extraction

Semiconducting wafers

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