research-article

LithoROC: lithography hotspot detection with explicit ROC optimization

Authors:

David Z. PanAuthors Info & Claims

ASPDAC '19: Proceedings of the 24th Asia and South Pacific Design Automation Conference

Pages 292 - 298

https://doi.org/10.1145/3287624.3288746

Published: 21 January 2019 Publication History

Abstract

As modern integrated circuits scale up with escalating complexity of layout design patterns, lithography hotspot detection, a key stage of physical verification to ensure layout finishing and design closure, has raised a higher demand on its efficiency and accuracy. Among all the hotspot detection approaches, machine learning distinguishes itself for achieving high accuracy while maintaining low false alarms. However, due to the class imbalance problem, the conventional practice which uses the accuracy and false alarm metrics to evaluate different machine learning models is becoming less effective. In this work, we propose the use of the area under the ROC curve (AUC), which provides a more holistic measure for imbalanced datasets compared with the previous methods. To systematically handle class imbalance, we further propose the surrogate loss functions for direct AUC maximization as a substitute for the conventional cross-entropy loss. Experimental results demonstrate that the new surrogate loss functions are promising to outperform the cross-entropy loss when applied to the state-of-the-art neural network model for hotspot detection.

References

[1]

C. A. Mack, "Thirty years of lithography simulation," in Optical Microlithography XVIII, vol. 5754. International Society for Optics and Photonics, 2004, pp. 1--13.

[2]

J. Xu, S. Sinha, and C. C. Chiang, "Accurate detection for process-hotspots with vias and incomplete specification," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2007, pp. 839--846.

Digital Library

[3]

Y.-T. Yu, Y.-C. Chan, S. Sinha, I. H.-R. Jiang, and C. Chiang, "Accurate process-hotspot detection using critical design rule extraction," in ACM/IEEE Design Automation Conference (DAC), 2012, pp. 1167--1172.

Digital Library

[4]

S.-Y. Lin, J.-Y. Chen, J.-C. Li, W.-Y. Wen, and S.-C. Chang, "A novel fuzzy matching model for lithography hotspot detection," in ACM/IEEE Design Automation Conference (DAC), 2013, pp. 68:1--68:6.

Digital Library

[5]

W.-Y. Wen, J.-C. Li, S.-Y. Lin, J.-Y. Chen, and S.-C. Chang, "A fuzzy-matching model with grid reduction for lithography hotspot detection," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 33, no. 11, pp. 1671--1680, 2014.

[6]

I. Nitta, Y. Kanazawa, T. Ishida, and K. Banno, "A fuzzy pattern matching method based on graph kernel for lithography hotspot detection," in Design-Process-Technology Co-optimization for Manufacturability XI, vol. 10148. International Society for Optics and Photonics, 2017.

[7]

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.

Digital Library

[8]

D. Ding, J. A. Torres, and D. Z. Pan, "High performance lithography hotspot detection with successively refined pattern identifications and machine learning," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 30 no. 11 pp. 1621--1634 2011.

Digital Library

[9]

D. Ding, B. Yu, J. Ghosh, and D. Z. Pan, "EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation," in IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), 2012, pp. 263--270.

[10]

Y.-T. Yu, G.-H. Lin, I.H.-R. Jiang, and C. Chiang, "Machine-learning-based hotspot detection using topological classification and critical feature extraction," in ACM/IEEE Design Automation Conference (DAC), 2013, pp. 671--676.

Digital Library

[11]

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 Proceedings of SPIE, vol. 9427, 2015.

[12]

Y.-T. Yu, G.-H. Lin, I. H.-R. 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

[13]

H. Zhang, B. Yu, and E. F. Y. Young, "Enabling online learning in lithography hotspot detection with information-theoretic feature optimization," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2016, pp. 47:1--47:8.

Digital Library

[14]

Y. Tomioka, T. Matsunawa, C. Kodama, and S. Nojima, "Lithography hotspot detection by two-stage cascade classifier using histogram of oriented light propagation," in IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), 2017, pp. 81--86.

[15]

H. Zhang, F. Zhu, H. Li, E. F. Y. Young, and B. Yu, "Bilinear lithography hotspot detection," in ACM International Symposium on Physical Design (ISPD), 2017, pp. 7--14.

Digital Library

[16]

J. W. Park, A. Torres, and X. Song, "Litho-aware machine learning for hotspot detection," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 37, no. 7, pp. 1510--1514, 2018.

[17]

T. Matsunawa, S. Nojima, and T. Kotani, "Automatic layout feature extraction for lithography hotspot detection based on deep neural network," in SPIE Advanced Lithography, vol. 9781, 2016.

[18]

M. Shin and J.-H. Lee, "Accurate lithography hotspot detection using deep convolutional neural networks," Journal of Micro/Nanolithography, MEMS, and MOEMS (JM3), vol. 15, no. 4, p. 043507, 2016.

[19]

H. Yang, L. Luo, J. Su, C. Lin, and B. Yu, "Imbalance aware lithography hotspot detection: a deep learning approach," Journal of Micro/Nanolithography, MEMS, and MOEMS (JM3), vol. 16, no. 3, p. 033504, 2017.

[20]

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 ACM/IEEE Design Automation Conference (DAC), 2017, pp. 62:1--62:6.

Digital Library

[21]

J. A. Swets and R. M. Pickett, Evaluation of diagnostic systems: methods from signal detection theory. New York : Academic Press, 1982.

[22]

D. M. Green and J. A. Swets, Signal detection theory and psychophysics. New York : Wiley, 1966.

[23]

D. K. McClish, "Analyzing a portion of the roc curve" Medical Decision Making, vol. 9, no. 3, pp. 190--195, 1989.

[24]

L. E. Dodd and M. S. Pepe, "Partial auc estimation and regression," Biometrics, vol. 59, no. 3, pp. 614--623, 2003.

[25]

N. Japkowicz and S. Stephen, "The class imbalance problem: A systematic study," Intelligent data analysis, vol. 6, no. 5, pp. 429--449, 2002.

[26]

M. Kubat and S. Mattwin, "Addressing the curse of imbalanced training sets: Onesided selection," in International Conference on Machine Learning (ICML), 1997, pp. 179--186.

[27]

M. Buda, A. Maki, and M. A. Mazurowski, "A systematic study of the class imbalance problem in convolutional neural networks," Neural Networks, vol. 106, pp. 249--259, 2018.

Digital Library

[28]

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "Smote: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321--357, 2002.

[29]

H. Han, W.-Y. Wang, and B.-H. Mao, "Borderline-smote: a new over-sampling method in imbalanced datasets learning," in International Conference on Intelligent Computing, 2005, pp. 878--887.

Digital Library

[30]

T. Jo and N. Japkowicz, "Class imbalances versus small disjuncts," ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 40--49, 2004.

Digital Library

[31]

H. Yang, L. Luo, J. Su, C. Lin, and B. Yu, "Imbalance aware lithography hotspot detection: A deep learning approach," in SPIE Advanced Lithography, vol. 10148, 2017.

[32]

Y. Lin, M. Li, Y. Watanabe, T. Kimura, T. Matsunawa, S. Nojima, and D. Z. Pan, "Data efficient lithography modeling with transfer learning and active data selection," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2018.

Digital Library

[33]

C. Elkan, "The foundations of cost-sensitive learning," in International Joint Conference on Artificial Intelligence (IJCAI), 2001, pp. 973--978.

Digital Library

[34]

G. M. Weiss, "Mining with rarity: a unifying framework," ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 7--19, 2004.

Digital Library

[35]

Y.-A. Chung, H.-T. Lin, and S.-W. Yang, "Cost-aware pre-training for multiclass cost-sensitive deep learning," in International Joint Conference on Artificial Intelligence (IJCAI), 2016, pp. 1411--1417.

Digital Library

[36]

S. H. Khan, M. Hayat, M. Bennamoun, F. A. Sohel, and R. Togneri, "Cost-sensitive learning of deep feature representations from imbalanced data," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3573--3587, 2018.

[37]

S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, "Training deep neural networks on imbalanced data sets," in International Joint Conference on Neural Networks (IJCNN), 2016, pp. 4368--4374.

[38]

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, "Brain tumor segmentation with deep neural networks," Medical image analysis, vol. 35, pp. 18--31, 2017.

[39]

C. M. Bishop et al., Pattern Recognition and Machine Learning. Springer New York, 2006, vol. 4, no. 4.

Digital Library

[40]

H. B. Mannand D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," Ann. Math. Statist., vol. 18, no. 1, pp. 50--60, 1947.

[41]

F. Wilcoxon, "Individual comparisons by ranking methods," Biometrics bulletin, vol. 1, no. 6, pp. 80--83, 1945.

[42]

J. A. Hanley and B. J. McNeil, "The meaning and use of the area under a receiver operating characteristic (roc) curve." Radiology, vol. 143, no. 1, pp. 29--36, 1982.

[43]

S. Wu, P. Flach, and C. Ferri, "An improved model selection heuristic for auc," in European Conference on Machine Learning, 2007, pp. 478--489.

Digital Library

[44]

W. Gao, R. Jin, S. Zhu, and Z.-H. Zhou, "One-pass auc optimization," in International Conference on Machine Learning (ICML), 2013, pp. III-906--III-914.

Digital Library

[45]

Y. Ding, P. Zhao, S. C. H. Hoi, and Y.-S. Ong, "An adaptive gradient method for online auc maximization," in AAAI Conference on Artificial Intelligence, 2015, pp. 2568--2574.

Digital Library

[46]

H. Steck, "Hinge rank loss and the area under the roc curve," in European Conference on Machine Learning. Springer Berlin Heidelberg, 2007, pp. 347--358.

Digital Library

[47]

P. Zhao, S. C. H. Hoi, R. Jin, and T. Yang, "Online auc maximization," in International Conference on Machine Learning (ICML), 2011, pp. 233--240.

Digital Library

[48]

C. Rudin and R. E. Schapire, "Margin-based ranking and an equivalence between adaboost and rankboost," Journal of Machine Learning Research, vol. 10, no. Oct, pp. 2193--2232, 2009.

Digital Library

[49]

L. Yan, R. H. Dodier, M. Mozer, and R. H. Wolniewicz, "Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic," in International Conference on Machine Learning (ICML), 2003, pp. 848--855.

Digital Library

[50]

I. J. Good, "Rational decisions," Journal of the Royal Statistical Society. Series B (Methodological), pp. 107--114, 1952.

[51]

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., "Tensorflow: a system for large-scale machine learning" in USENIX Symposium on Operating Systems Design and Implementation (OSDI), vol. 16, 2016, pp. 265--283.

Digital Library

[52]

A. J. 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.

Digital Library

Cited By

Tseng JChien JLee E(2024)Advanced defect recognition on scanning electron microscope images: a two-stage strategy based on deep convolutional neural networks for hotspot monitoringJournal of Micro/Nanopatterning, Materials, and Metrology10.1117/1.JMM.23.4.04420123:04Online publication date: 1-Oct-2024
https://doi.org/10.1117/1.JMM.23.4.044201
Kim JLim JLee JKim TNam YKim KKim D(2024)Hotspot Prediction: SEM Image Generation With Potential Lithography HotspotsIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2023.332778437:1(103-114)Online publication date: Feb-2024
https://doi.org/10.1109/TSM.2023.3327784
Pan JLin XXu JChen YZhuo C(2024)Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature SelectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.333284143:5(1484-1496)Online publication date: May-2024
https://doi.org/10.1109/TCAD.2023.3332841
Show More Cited By

Recommendations

Palmprint and Finger-Knuckle-Print for efficient person recognition based on Log-Gabor filter response

Person recognition systems based on biometrics are being increasingly utilized in any applications to enhance the security of physical and logical access systems. A number of biometric traits exist and are in use in various applications. Each biometric ...
Multi-factor biometrics for authentication: a false sense of security
MM&Sec '10: Proceedings of the 12th ACM workshop on Multimedia and security

Multi-factor biometric authentications have been proposed recently to strengthen security and/or privacy of biometric systems in addition to enhancing authentication accuracy. An important approach to multi-factor biometric authentication is to apply ...
Biometric recognition using finger and palm vein images

In recent times, biometrics is the best alternative for the token-based and knowledge-based security systems. Out of the existing biometric modalities, the vascular biometric modalities are preferred for authenticating the person, because of its ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASPDAC '19: Proceedings of the 24th Asia and South Pacific Design Automation Conference

January 2019

794 pages

ISBN:9781450360074

DOI:10.1145/3287624

General Chair:
Toshiyuki Shibuya
Fujitsu Laboratories

Copyright © 2019 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]

Sponsors

SIGDA: ACM Special Interest Group on Design Automation

In-Cooperation

IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE CAS
IEEE CEDA
IPSJ SIG-SLDM: Information Processing Society of Japan, SIG System LSI Design Methodology

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 January 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

ASPDAC '19

Sponsor:

SIGDA

ASPDAC '19: 24th Asia and South Pacific Design Automation Conference

January 21 - 24, 2019

Tokyo, Japan

Acceptance Rates

Overall Acceptance Rate 466 of 1,454 submissions, 32%

Upcoming Conference

ASPDAC '25

Sponsor:
sigda

30th Asia and South Pacific Design Automation Conference

January 20 - 23, 2025

Tokyo , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
283
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tseng JChien JLee E(2024)Advanced defect recognition on scanning electron microscope images: a two-stage strategy based on deep convolutional neural networks for hotspot monitoringJournal of Micro/Nanopatterning, Materials, and Metrology10.1117/1.JMM.23.4.04420123:04Online publication date: 1-Oct-2024
https://doi.org/10.1117/1.JMM.23.4.044201
Kim JLim JLee JKim TNam YKim KKim D(2024)Hotspot Prediction: SEM Image Generation With Potential Lithography HotspotsIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2023.332778437:1(103-114)Online publication date: Feb-2024
https://doi.org/10.1109/TSM.2023.3327784
Pan JLin XXu JChen YZhuo C(2024)Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature SelectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.333284143:5(1484-1496)Online publication date: May-2024
https://doi.org/10.1109/TCAD.2023.3332841
Chen RHu SChen ZZhu SYu BLi PChen CHuang YHao J(2023)A Unified Framework for Layout Pattern Analysis With Deep Causal EstimationIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319236342:4(1199-1211)Online publication date: Apr-2023
https://doi.org/10.1109/TCAD.2022.3192363
Wang ZShen YZhao WBai YChen GFarnia FYu B(2023)DiffPattern: Layout Pattern Generation via Discrete Diffusion2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10248009(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10248009
Wen LZhu YYe LChen GYu BLiu JXu CMitra TYoung EXiong J(2022)LayouTransformerProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3549350(1-9)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3549350
Chen RZhong WYang HGeng HYang FZeng XYu B(2022)Faster Region-Based Hotspot DetectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.302166341:3(669-680)Online publication date: Mar-2022
https://doi.org/10.1109/TCAD.2020.3021663
Sumedha Rohilla R(2022)Exploring Vision Transformer model for detecting Lithography Hotspots2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)10.1109/CENTCON56610.2022.10051370(200-205)Online publication date: 22-Dec-2022
https://doi.org/10.1109/CENTCON56610.2022.10051370
Ma Y(2022)Machine Learning for Testability PredictionMachine Learning Applications in Electronic Design Automation10.1007/978-3-031-13074-8_6(151-180)Online publication date: 10-Aug-2022
https://doi.org/10.1007/978-3-031-13074-8_6
Diallo MMokeddem SBraud AFrey GLachiche N(2021)Identifying Benchmarks for Failure Prediction in Industry 4.0Informatics10.3390/informatics80400688:4(68)Online publication date: 30-Sep-2021
https://doi.org/10.3390/informatics8040068
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents