Mr. Yu-Tang Hu Profile

Yu-Tang Hu

at Macronix International Co Ltd

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 10 April 2024 Poster + Paper

Advanced regression OPC model setup

Tony Hu, Terry Hsuan, Elvis Yang, Li-Jin Chen, Tony Chao, Y. Liao, Elsley Tan

Proceedings Volume 12954, 1295419 (2024) https://doi.org/10.1117/12.3008958

KEYWORDS: Autoregressive models, Optical proximity correction, Performance modeling, Education and training, 3D modeling, Calibration, Neurons, Data modeling, Modeling, Overfitting

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Proceedings Article | 10 April 2024 Presentation + Paper

Enhancing lithography printability through deep generative models for layout re-targeting

Weilun Chiu, Tony Hu, Terry Hsuan, Elvis Yang, T. Yang, K. Chen

Proceedings Volume 12954, 129540Q (2024) https://doi.org/10.1117/12.3008955

KEYWORDS: Data modeling, Deep learning, Education and training, Matrices, Gallium nitride, Diffusion, Simulations, Optical proximity correction, Performance modeling, 3D modeling

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Proceedings Article | 28 April 2023 Poster + Paper

Lithography hotspot correction on post-OPC layout using generative adversarial networks

Weilun Ciou, Tony Hu, Fz Cheng, Y. Tsai, Terry Hsuan, Elvis Yang, T. Yang, K. Chen

Proceedings Volume 12495, 124951Y (2023) https://doi.org/10.1117/12.2654456

KEYWORDS: Machine learning, Data modeling, Contour modeling, Optical proximity correction, Lithography, Critical dimension metrology, Image segmentation, Deep learning

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SPIE Journal Paper | 23 November 2022

Machine learning optical proximity correction with generative adversarial networks

Weilun Ciou, Tony Hu, Yi-Yen Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, Kuang-Chao Chen

JM3, Vol. 21, Issue 04, 041606, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMM.21.4.041606

KEYWORDS: Photomasks, Education and training, Optical proximity correction, Data modeling, Networks, Gallium nitride, Machine learning, Computer simulations, Semiconducting wafers, Image processing

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Proceedings Article | 26 May 2022 Presentation + Paper

Machine learning OPC with generative adversarial networks

Weilun Ciou, Tony Hu, Yi-Yien Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, Kuang-Chao Chen

Proceedings Volume 12052, 120520Z (2022) https://doi.org/10.1117/12.2606715

KEYWORDS: Data modeling, Photomasks, Optical proximity correction, Machine learning, Performance modeling, Computer simulations, Detection and tracking algorithms, Computer programming

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SPIE Journal Paper | 12 November 2021

Sub-resolution assist feature placement with generative adversarial network

Weilun Ciou, Tony Hu, Yi-Yen Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, Kuang-Chao Chen

JM3, Vol. 20, Issue 04, 041209, (November 2021) https://doi.org/10.1117/12.10.1117/1.JMM.20.4.041209

KEYWORDS: SRAF, Gallium nitride, Data modeling, Machine learning, Molybdenum, Performance modeling, Lithography, Image processing, Source mask optimization, Photomasks

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Background: As the design layout of integrated circuits (ICs) is continually scaling down, sub-resolution assist features (SRAF) have been extensively used in resolution enhancement technique applications to enhance lithography printing fidelity and widen the manufacturing process window (PW). With conventional SRAF insertion techniques, rule-based SRAF (RB-SRAF) and model-based SRAF (MB-SRAF) methods have since been widely adopted. Aim: The typical RB-SRAF is an efficient method to generate SRAFs consistently for simple designs but cannot be optimized for multiple critical patterns or complex layout schemes. Although MB-SRAF is able to achieve better PW as well as reducing conflicts between placement rules and clean-up rules, many iterations for convergence and extremely high computational costs are required. The explosion of machine learning techniques could facilitate the complex processes of mask optimization, such as SRAF insertion. Approach: Generative adversarial network was studied on a Via layer of advanced 3D NAND flash memory, by training target images and Inverse Lithography Technology (ILT) images of target patterns. GAN models, pix2pix, and CycleGAN, were first trained and then utilized to synthesize realistic ILT images. Results: These ILT images were eventually translated to polygons of SRAF with simplification process and mask manufacturing rules check constraints. The simulation results demonstrate that CycleGAN approach can place SRAF with comparable performance to mask optimization (MO) result which was optimized by the Tachyon source-mask optimizer (SMO). Conclusions: Methodologies of SRAF placement based on GANs were demonstrated in this paper. While traditional MO approach showed the best lithography performance, CycleGAN in particular was close behind and had much better performance than conventional RB-SRAF approach, whilst achieving good stability for different layout environments. Most importantly, the efficiency of SRAF insertion can be enhanced significantly through GANs.

Proceedings Article | 23 March 2021 Presentation + Paper

Rule-based hotspot correction using a pattern matching flow

Bradley Falch, Tony Hu, Terry Hsuan, Elvis Yang, T. Yang, K. C. Chen, Rex Cheng, Linghui Wu, John Tsai, Lisa Huang, Elsley Tan

Proceedings Volume 11614, 1161408 (2021) https://doi.org/10.1117/12.2585531

KEYWORDS: Lithography, Process modeling, Photomasks, Lead, Etching

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Proceedings Article | 22 February 2021 Presentation + Paper

SRAF placement with generative adversarial network

Weilun Ciou, Tony Hu, Y. Tsai, Terry Hsuan, Elvis Yang, T. Yang, K. Chen

Proceedings Volume 11613, 1161305 (2021) https://doi.org/10.1117/12.2581334

KEYWORDS: SRAF, Source mask optimization, Resolution enhancement technologies, Manufacturing, Lithography, 3D modeling, Printing, Photomasks, Molybdenum, Model-based design

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Showing 5 of 8 publications

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