research-article

A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

Authors:

Tetsuaki Matsunawa,

Shigeki Nojima,

Chikaaki Kodama,

Toshiya Kotani,

David Z. PanAuthors Info & Claims

ISPD '16: Proceedings of the 2016 on International Symposium on Physical Design

Pages 161 - 168

https://doi.org/10.1145/2872334.2872357

Published: 03 April 2016 Publication History

Abstract

Sub-Resolution Assist Feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model- based SRAF generation has been widely used to achieve high accuracy but it is known to be time consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. This paper proposes the first ma- chine learning based framework for fast yet consistent SRAF generation with high quality of results. Our technical con- tributions include robust feature extraction, novel feature compaction, model training for SRAF classification and pre- diction, and the final SRAF generation with consideration of practical mask manufacturing constraints. Experimental re- sults demonstrate that, compared with commercial Calibre tool, our machine learning based SRAF generation obtains 10X speed up and comparable performance in terms of edge placement error (EPE) and process variation (PV) band.

References

[1]

Y. Ping, S. McGowan, Y. Gong, Y. M. Foong, J. Liu, J. Qiu, V. Shu, B. Yan, J. Ye, P. Li et al., "Process window enhancement using advanced ret techniques for 20nm contact layer," in Proc. of SPIE, 2014, pp. 90 521N-90 521N.

[2]

S. Banerjee, Z. Li, and S. R. Nassif, "ICCAD-2013 CAD contest in mask optimization and benchmark suite," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2013, pp. 271--274.

[3]

J.-R. Gao, X. Xu, Y. Bei, and D. Z. Pan, "MOSAIC: Mask optimizing solution with process window aware inverse correction," in ACM/IEEE Design Automation Conference (DAC), 2014, pp. 52:1--52:6.

[4]

Y.-H. Su, Y.-C. Huang, L.-C. Tsai, Y.-W. Chang, and S. Banerjee, "Fast lithographic mask optimization considering process variation," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2014, pp. 230--237.

[5]

A. Awad, A. Takahashi, S. Tanaka, and C. Kodama, "A fast process variation and pattern fidelity aware mask optimization algorithm," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2014, pp. 238--245.

[6]

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

[7]

J.-H. Jun, M. Park, C. Park, H. Yang, D. Yim, M. Do, D. Lee, T. Kim, J. Choi, G. Luk-Pat et al., "Layout optimization with assist features placement by model based rule tables for 2x node random contact," in Proc. of SPIE, 2015, pp. 94 270D--94 270D.

[8]

C. Kodama, T. Kotani, S. Nojima, and S. Mimotogi, "Sub-resolution assist feature arranging method and computer program product and manufacturing method of semiconductor device," Aug. 19 2014, US Patent 8,809,072.

[9]

K. Sakajiri, A. Tritchkov, and Y. Granik, "Model-based sraf insertion through pixel-based mask optimization at 32nm and beyond," in Proc. of SPIE, 2008, pp. 702 811--702 811.

[10]

R. Viswanathan, J. T. Azpiroz, and P. Selvam, "Process optimization through model based sraf printing prediction," in Proc. of SPIE, 2012, pp. 83 261A-83 261A.

[11]

J. Ye, Y. Cao, and H. Feng, "System and method for model-based sub-resolution assist feature generation," Feb. 1 2011, US Patent 7,882,480.

[12]

S. D. Shang, L. Swallow, and Y. Granik, "Model-based sraf insertion," Oct. 11 2011, US Patent 8,037,429.

[13]

L. Pang, Y. Liu, and D. Abrams, "Inverse lithography technology (ilt): a natural solution for model-based sraf at 45nm and 32nm," in Proc. of SPIE, 2007, pp. 660 739--660 739.

[14]

B.-S. Kim, Y.-H. Kim, S.-H. Lee, S.-I. Kim, S.-R. Ha, J. Kim, and A. Tritchkov, "Pixel-based sraf implementation for 32nm lithography process," in Proc. of SPIE, 2008, pp. 71 220T-71 220T.

[15]

J. A. Torres, "ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2012.

[16]

D. Ding, X. Wu, J. Ghosh, and D. Z. Pan, "Machine learning based lithographic hotspot detection with critical-feature extraction and classification," in IEEE International Conference on IC Design and Technology (ICICDT), 2009.

[17]

D. G. Drmanac, F. Liu, and L.-C. Wang, "Predicting variability in nanoscale lithography processes," in ACM/IEEE Design Automation Conference (DAC), 2009, pp. 545--550.

[18]

Y. Yu, G. Lin, I. Jiang, and C. Chiang, "Machine-learning-based hotspot detection using topological classification and critical feature extraction," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 34, no. 3, pp. 460--470, 2015.

Digital Library

[19]

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. of SPIE, 2015, pp. 94 270S-94 270S.

[20]

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

[21]

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

[22]

T. Matsunawa, B. Yu, and D. Z. Pan, "Optical proximity correction with hierarchical bayes model," in Proc. of SPIE, 2015, pp. 94 260X-94 260X. [23] X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, "Fast pixel-based optical proximity correction based on nonparametric kernel regression," Journal of Microlithography, Microfabrication and Microsystems, vol. 13, no. 4, pp. 043 007-043 007, 2014.

[23]

P. Gupta, "What is process window"? SIGDA Newsl., vol. 40, no. 8, pp. 1--1, Aug. 2010.

Digital Library

[24]

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, "The elements of statistical learning: data mining, inference and prediction," The Mathematical Intelligencer, vol. 27, no. 2, pp. 83--85, 2005.

[25]

S. Behnel, R. Bradshaw, C. Citro, L. Dalcin, D. Seljebotn, and K. Smith, "Cython: The best of both worlds," Computing in Science Engineering, vol. 13, no. 2, pp. 31--39, 2011.

Digital Library

[26]

C. Goutte and E. Gaussier, "A probabilistic interpretation of precision, recall and f-score, with implication for evaluation," in Advances in information retrieval. Springer, 2005, pp. 345--359.

Cited By

Ngo ADey BHalder SDe Gendt SWang C(2023)Machine Learning-Based Edge Placement Error Analysis and Optimization: A Systematic ReviewIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2022.321732636:1(1-13)Online publication date: Feb-2023
https://doi.org/10.1109/TSM.2022.3217326
Pan JChang CXie ZHu JChen YMitra TYoung EXiong J(2022)Robustify ML-Based Lithography Hotspot DetectorsProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3549389(1-7)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3549389
Yang HLin YYu B(2022)Machine Learning for Mask Synthesis and VerificationMachine Learning Applications in Electronic Design Automation10.1007/978-3-031-13074-8_15(425-470)Online publication date: 10-Aug-2022
https://doi.org/10.1007/978-3-031-13074-8_15
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Index Terms

A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation
1. Hardware
  1. Very large scale integration design
    1. VLSI design manufacturing considerations

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Published In

cover image ACM Conferences

ISPD '16: Proceedings of the 2016 on International Symposium on Physical Design

April 2016

180 pages

ISBN:9781450340397

DOI:10.1145/2872334

General Chair:
Evangeline Young
The Chinese University of Hong Kong
,
Program Chair:
Mustafa Ozdal
Bilkent University

Copyright © 2016 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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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

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Published: 03 April 2016

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ISPD'16: International Symposium on Physical Design

April 3 - 6, 2016

California, Santa Rosa, USA

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Overall Acceptance Rate 62 of 172 submissions, 36%

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Cited By

Ngo ADey BHalder SDe Gendt SWang C(2023)Machine Learning-Based Edge Placement Error Analysis and Optimization: A Systematic ReviewIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2022.321732636:1(1-13)Online publication date: Feb-2023
https://doi.org/10.1109/TSM.2022.3217326
Pan JChang CXie ZHu JChen YMitra TYoung EXiong J(2022)Robustify ML-Based Lithography Hotspot DetectorsProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3549389(1-7)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3549389
Yang HLin YYu B(2022)Machine Learning for Mask Synthesis and VerificationMachine Learning Applications in Electronic Design Automation10.1007/978-3-031-13074-8_15(425-470)Online publication date: 10-Aug-2022
https://doi.org/10.1007/978-3-031-13074-8_15
Goswami PBhatia D(2021)Congestion Prediction in FPGA Using Regression Based Learning MethodsElectronics10.3390/electronics1016199510:16(1995)Online publication date: 18-Aug-2021
https://doi.org/10.3390/electronics10161995
Tryasoguzov PKuzovkov AKarandashev ITeplov G(2021)Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)Optical Memory and Neural Networks10.3103/S1060992X2104005630:4(291-297)Online publication date: 29-Dec-2021
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Huang GHu JHe YLiu JMa MShen ZWu JXu YZhang HZhong KNing XMa YYang HYu BYang HWang Y(2021)Machine Learning for Electronic Design Automation: A SurveyACM Transactions on Design Automation of Electronic Systems10.1145/345117926:5(1-46)Online publication date: 5-Jun-2021
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Liu KZhang JTan BFeng D(2021)Can We Trust Machine Learning for Electronic Design Automation?2021 IEEE 34th International System-on-Chip Conference (SOCC)10.1109/SOCC52499.2021.9739485(135-140)Online publication date: 14-Sep-2021
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Geng HZhong WYang HMa YMitra JYu B(2020)SRAF Insertion via Supervised Dictionary LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.294356839:10(2849-2859)Online publication date: Oct-2020
https://doi.org/10.1109/TCAD.2019.2943568
Yang HLi SDeng ZMa YYu BYoung E(2020)GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial NetsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.293932939:10(2822-2834)Online publication date: Oct-2020
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Ye WAlawieh MLin YPan D(2019)LithoGANProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317852(1-6)Online publication date: 2-Jun-2019
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