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AdaOPC: A Self-Adaptive Mask Optimization Framework for Real Design Patterns

Published: 22 December 2022 Publication History

Abstract

Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks. We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency.

References

[1]
D. Z. Pan, B. Yu, and J.-R. Gao, "Design for manufacturing with emerging nanolithography," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 10, pp. 1453--1472, 2013.
[2]
J.-S. Park, C.-H. Park, S.-U. Rhie, Y.-H. Kim, M.-H. Yoo, J.-T. Kong, H.-W. Kim, and S.-I. Yoo, "An efficient rule-based opc approach using a drc tool for 0.18/spl mu/m asic," in Proceedings IEEE 2000 First International Symposium on Quality Electronic Design (Cat. No. PR00525). IEEE, 2000, pp. 81--85.
[3]
J. Kuang, W.-K. Chow, and E. F. Young, "A robust approach for process variation aware mask optimization," in 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2015, pp. 1591--1594.
[4]
Y.-H. Su, Y.-C. Huang, L.-C. Tsai, Y.-W. Chang, and S. Banerjee, "Fast lithographic mask optimization considering process variation," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 8, pp. 1345--1357, 2016.
[5]
T. Matsunawa, B. Yu, and D. Z. Pan, "Optical proximity correction with hierarchical bayes model," in Optical Microlithography XXVIII, vol. 9426. International Society for Optics and Photonics, 2015, p. 94260X.
[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.
[7]
Y. Ma, W. Zhong, S. Hu, J.-R. Gao, J. Kuang, J. Miao, and B. Yu, "A unified framework for simultaneous layout decomposition and mask optimization," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 12, pp. 5069--5082, 2020.
[8]
Z. Yu, G. Chen, Y. Ma, and B. Yu, "A gpu-enabled level set method for mask optimization," in 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021, pp. 1835--1838.
[9]
H. Yang, S. Li, Z. Deng, Y. Ma, B. Yu, and E. F. Young, "Gan-opc: Mask optimization with lithography-guided generative adversarial nets," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 10, pp. 2822--2834, 2019.
[10]
B. Jiang, H. Zhang, J. Yang, and E. F. Young, "A fast machine learning-based mask printability predictor for opc acceleration," in Proceedings of the 24th Asia and South Pacific Design Automation Conference, 2019, pp. 412--419.
[11]
H. Geng, W. Zhong, H. Yang, Y. Ma, J. Mitra, and B. Yu, "Sraf insertion via supervised dictionary learning," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 10, pp. 2849--2859, 2019.
[12]
G. Chen, W. Chen, Q. Sun, Y. Ma, H. Yang, and B. Yu, "Damo: Deep agile mask optimization for full chip scale," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021.
[13]
G. Chen, Z. Yu, H. Liu, Y. Ma, and B. Yu, "Develset: Deep neural level set for instant mask optimization," in 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2021, pp. 1--9.
[14]
B. Jiang, L. Liu, Y. Ma, H. Zhang, B. Yu, and E. F. Young, "Neural-ilt: Migrating ilt to neural networks for mask printability and complexity co-optimization," in 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2020, pp. 1--9.
[15]
H. H. Hopkins, "The concept of partial coherence in optics," Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, vol. 208, no. 1093, pp. 263--277, 1951.
[16]
N. B. Cobb, Fast optical and process proximity correction algorithms for integrated circuit manufacturing. University of California, Berkeley, 1998.
[17]
J.-R. Gao, X. Xu, B. Yu, and D. Z. Pan, "Mosaic: Mask optimizing solution with process window aware inverse correction," in 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC). IEEE, 2014, pp. 1--6.
[18]
Y. Watanabe, T. Kimura, T. Matsunawa, and S. Nojima, "Accurate lithography simulation model based on convolutional neural networks," in Optical Microlithography XXX, vol. 10147. International Society for Optics and Photonics, 2017, p. 101470K.
[19]
W. Ye, M. B. Alawieh, Y. Lin, and D. Z. Pan, "Lithogan: End-to-end lithography modeling with generative adversarial networks," in 2019 56th ACM/IEEE Design Automation Conference (DAC). IEEE, 2019, pp. 1--6.
[20]
H.-C. Shao, C.-Y. Peng, J.-R. Wu, C.-W. Lin, S.-Y. Fang, P.-Y. Tsai, and Y.-H. Liu, "From ic layout to die photograph: A cnn-based data-driven approach," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 5, pp. 957--970, 2020.
[21]
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2223--2232.
[22]
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770--778.
[23]
Y. A. Malkov and D. A. Yashunin, "Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs," IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 4, pp. 824--836, 2018.
[24]
R. Hadsell, S. Chopra, and Y. LeCun, "Dimensionality reduction by learning an invariant mapping," in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol. 2. IEEE, 2006, pp. 1735--1742.
[25]
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, "Unsupervised feature learning via non-parametric instance discrimination," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3733--3742.
[26]
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, "Learning deep representations by mutual information estimation and maximization," arXiv preprint arXiv:1808.06670, 2018.
[27]
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, "A simple framework for contrastive learning of visual representations," in International conference on machine learning. PMLR, 2020, pp. 1597--1607.
[28]
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, "Supervised contrastive learning," Advances in Neural Information Processing Systems, vol. 33, pp. 18 661--18 673, 2020.
[29]
N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun, "Fast convolutional nets with fbfft: A gpu performance evaluation," arXiv preprint arXiv:1412.7580, 2014.
[30]
S. Banerjee, Z. Li, and S. R. Nassif, "Iccad-2013 cad contest in mask optimization and benchmark suite," in 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2013, pp. 271--274.
[31]
T. Ajayi and D. Blaauw, "Openroad: Toward a self-driving, open-source digital layout implementation tool chain," in Proceedings of Government Microcircuit Applications and Critical Technology Conference, 2019.

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  • (2024)ILILTProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694394(56319-56331)Online publication date: 21-Jul-2024
  • (2024)EMOGen: Enhancing Mask Optimization via Pattern GenerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655680(1-6)Online publication date: 23-Jun-2024
  • (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
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cover image ACM Conferences
ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
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-EDS: Electronic Devices Society
  • IEEE CAS
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Association for Computing Machinery

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Published: 22 December 2022

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ICCAD '22
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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design
October 30 - November 3, 2022
California, San Diego

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Cited By

View all
  • (2024)ILILTProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694394(56319-56331)Online publication date: 21-Jul-2024
  • (2024)EMOGen: Enhancing Mask Optimization via Pattern GenerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655680(1-6)Online publication date: 23-Jun-2024
  • (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
  • (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
  • (2023)LithoBenchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667437(30243-30254)Online publication date: 10-Dec-2023
  • (2023)Machine Learning in EDA: When and How2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299822(1-6)Online publication date: 10-Sep-2023
  • (2023)Bit-Level Quantization for Efficient Layout Hotspot Detection2023 International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA59274.2023.10218502(465-470)Online publication date: 8-May-2023
  • (2023)OpenILT: An Open Source Inverse Lithography Technique Framework (Invited Paper)2023 IEEE 15th International Conference on ASIC (ASICON)10.1109/ASICON58565.2023.10396314(1-4)Online publication date: 24-Oct-2023

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