research-article

GAN-OPC: mask optimization with lithography-guided generative adversarial nets

Authors:

Evangeline F. Y. YoungAuthors Info & Claims

DAC '18: Proceedings of the 55th Annual Design Automation Conference

Article No.: 131, Pages 1 - 6

https://doi.org/10.1145/3195970.3196056

Published: 24 June 2018 Publication History

Abstract

Mask optimization has been a critical problem in the VLSI design flow due to the mismatch between the lithography system and the continuously shrinking feature sizes. Optical proximity correction (OPC) is one of the prevailing resolution enhancement techniques (RETs) that can significantly improve mask printability. However, in advanced technology nodes, the mask optimization process consumes more and more computational resources. In this paper, we develop a generative adversarial network (GAN) model to achieve better mask optimization performance. We first develop an OPC-oriented GAN flow that can learn target-mask mapping from the improved architecture and objectives, which leads to satisfactory mask optimization results. To facilitate the training process and ensure better convergence, we also propose a pre-training procedure that jointly trains the neural network with inverse lithography technique (ILT). At convergence, the generative network is able to create quasi-optimal masks for given target circuit patterns and fewer normal OPC steps are required to generate high quality masks. Experimental results show that our flow can facilitate the mask optimization process as well as ensure a better printability.

References

[1]

D. Z. Pan, B. Yu, and J.-R. Gao, "Design for manufacturing with emerging nanolithography," IEEE TCAD, vol. 32, no. 10, pp. 1453--1472, 2013.

Digital Library

[2]

B. Yu, X. Xu, S. Roy, Y. Lin, J. Ou, and D. Z. Pan, "Design for manufacturability and reliability in extreme-scaling VLSI," Science China Information Sciences, pp. 1--23, 2016.

[3]

A. Awad, A. Takahashi, S. Tanaka, and C. Kodama, "A fast process variation and pattern fidelity aware mask optimization algorithm," in Proc. ICCAD, 2014, pp. 238--245.

Digital Library

[4]

J. Kuang, W.-K. Chow, and E. F. Y. Young, "A robust approach for process variation aware mask optimization," in Proc. DATE, 2015, pp. 1591--1594.

Digital Library

[5]

Y.-H. Su, Y.-C. Huang, L.-C. Tsai, Y.-W. Chang, and S. Banerjee, "Fast lithographic mask optimization considering process variation," IEEE TCAD, vol. 35, no. 8, pp. 1345--1357, 2016.

Digital Library

[6]

A. Poonawala and P. Milanfar, "Mask design for optical microlithography-an inverse imaging problem," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 774--788, 2007.

Digital Library

[7]

J.-R. Gao, X. Xu, B. Yu, and D. Z. Pan, "MOSAIC: Mask optimizing solution with process window aware inverse correction," in Proc. DAC, 2014, pp. 52:1--52:6.

Digital Library

[8]

Y. Ma, J.-R. Gao, J. Kuang, J. Miao, and B. Yu, "A unified framework for simultaneous layout decomposition and mask optimization," in Proc. ICCAD, 2017, pp. 81--88.

Digital Library

[9]

R. Viswanathan, J. T. Azpiroz, and P. Selvam, "Process optimization through model based SRAF printing prediction," in SPIE Advanced Lithography, vol. 8326, 2012.

[10]

T. Matsunawa, J.-R. Gao, B. Yu, and D. Z. Pan, "A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction," in Proc. SPIE, vol. 9427, 2015.

[11]

H. Zhang, B. Yu, and E. F. Y. Young, "Enabling online learning in lithography hotspot detection with information-theoretic feature optimization," in Proc. ICCAD, 2016, pp. 47:1--47:8.

Digital Library

[12]

H. Yang, L. Luo, J. Su, C. Lin, and B. Yu, "Imbalance aware lithography hotspot detection: a deep learning approach," JM3, vol. 16, no. 3, p. 033504, 2017.

[13]

H. Yang, J. Su, Y. Zou, B. Yu, and E. F. Y. Young, "Layout hotspot detection with feature tensor generation and deep biased learning," in Proc. DAC, 2017, pp. 62:1--62:6.

Digital Library

[14]

T. Matsunawa, B. Yu, and D. Z. Pan, "Optical proximity correction with hierarchical bayes model," JM3, vol. 15, no. 2, p. 021009, 2016.

[15]

A. Gu and A. Zakhor, "Optical proximity correction with linear regression," IEEE TSM, vol. 21, no. 2, pp. 263--271, 2008.

[16]

R. Luo, "Optical proximity correction using a multilayer perceptron neural network," Journal of Optics, vol. 15, no. 7, p. 075708, 2013.

[17]

S. Choi, S. Shim, and Y. Shin, "Machine learning (ML)-guided OPC using basis functions of polar fourier transform," in Proc. SPIE, vol. 9780, 2016.

[18]

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," in Proc. NIPS, 2014, pp. 2672--2680.

Digital Library

[19]

H. Hopkins, "The concept of partial coherence in optics," in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 208, no. 1093. The Royal Society, 1951, pp. 263--277.

[20]

N. B. Cobb, "Fast optical and process proximity correction algorithms for integrated circuit manufacturing," Ph.D. dissertation, University of California at Berkeley, 1998.

Digital Library

[21]

J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber, "Stacked convolutional auto-encoders for hierarchical feature extraction," Artificial Neural Networks and Machine Learning-ICANN 2011, pp. 52--59, 2011.

Digital Library

[22]

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean et al., "TensorFlow: A system for large-scale machine learning," in Proc. OSDI, 2016, pp. 265--283.

Digital Library

[23]

S. Banerjee, Z. Li, and S. R. Nassif, "ICCAD-2013 CAD contest in mask optimization and benchmark suite," in Proc. ICCAD, 2013, pp. 271--274.

Digital Library

Cited By

Yin SZhao WXie LChen HMa YHo TYu BHui-Ru Jiang IPosser G(2024)FuILT: Full Chip ILT System With Boundary HealingProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633315(13-20)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3626184.3633315
Zheng SXiao GYan GDong MLi YChen HMa YYu BWong M(2024)Model-based OPC Extension in OpenILT2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617951(568-573)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617951
Chen HXu SLin KSong Y(2024)Mask optimization based on conditional generative adversarial nets2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE)10.1109/ECIE61885.2024.10626926(590-593)Online publication date: 24-May-2024
https://doi.org/10.1109/ECIE61885.2024.10626926
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cover image ACM Conferences

DAC '18: Proceedings of the 55th Annual Design Automation Conference

June 2018

1089 pages

ISBN:9781450357005

DOI:10.1145/3195970

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IEEE Council on Electronic Design Automation (CEDA)

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Published: 24 June 2018

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DAC '18: The 55th Annual Design Automation Conference 2018

June 24 - 29, 2018

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Yin SZhao WXie LChen HMa YHo TYu BHui-Ru Jiang IPosser G(2024)FuILT: Full Chip ILT System With Boundary HealingProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633315(13-20)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3626184.3633315
Zheng SXiao GYan GDong MLi YChen HMa YYu BWong M(2024)Model-based OPC Extension in OpenILT2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617951(568-573)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617951
Chen HXu SLin KSong Y(2024)Mask optimization based on conditional generative adversarial nets2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE)10.1109/ECIE61885.2024.10626926(590-593)Online publication date: 24-May-2024
https://doi.org/10.1109/ECIE61885.2024.10626926
Yu FZhang LLv BMo J(2024)A composite algorithm of lithography process in AlGaN/GaN-on-SiC HEMTs manufacturingMaterials Science and Engineering: B10.1016/j.mseb.2023.117065300(117065)Online publication date: Feb-2024
https://doi.org/10.1016/j.mseb.2023.117065
Zheng HLi SCheng WYuan SWang X(2023)Phase defect characterization using generative adversarial networks for extreme ultraviolet lithographyApplied Optics10.1364/AO.48035662:5(1243)Online publication date: 6-Feb-2023
https://doi.org/10.1364/AO.480356
Zheng CZhao GSo P(2023)Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Two-Photon Neural Lithography SimulatorSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618251(1-9)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618251
Shao HLin CFang STakahashi A(2023)Data-Driven Approaches for Process Simulation and Optical Proximity CorrectionProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568362(721-726)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568362
Zhao YXu SPan J(2023)Fast mask optimization based on self-calibrated convolutionsThird International Conference on Optics and Image Processing (ICOIP 2023)10.1117/12.2689153(33)Online publication date: 1-Aug-2023
https://doi.org/10.1117/12.2689153
URARD MPAQUET CBEYLIER CPENA JCAPLIER ADALLA MURA MBANGE RGUIZZETTI R(2023)Training dataset optimization for deep learning applied to optical proximity correction on non-regular hole masks38th European Mask and Lithography Conference (EMLC 2023)10.1117/12.2675612(24)Online publication date: 5-Oct-2023
https://doi.org/10.1117/12.2675612
Liu MYang HKhailany BRen H(2023)An Adversarial Active Sampling-Based Data Augmentation Framework for AI-Assisted Lithography Modeling2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323949(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323949
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