research-article

Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning

Authors:

Evangeline F. Y. YoungAuthors Info & Claims

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

Article No.: 62, Pages 1 - 6

https://doi.org/10.1145/3061639.3062270

Published: 18 June 2017 Publication History

Abstract

Detecting layout hotspots is one of the key problems in physical verification flow. Although machine learning solutions show benefits over lithography simulation and pattern matching based methods, it is still hard to select a proper model for large scale problems and it is inevitable that performance degradation will occur. To overcome these issues, in this paper we develop a deep learning framework for high performance and large scale hotspot detection. First, feature tensor generation is proposed to extract representative layout features that fit well with convolutional neural networks while keeping the spatial relationship of the original layout pattern with minimal information loss. Second, we propose a biased learning algorithm to train the convolutional neural network to further improve detection accuracy with small false alarm penalties. Experimental results show that our framework outperforms previous machine learning-based hotspot detectors in both the ICCAD 2012 Contest benchmarks and large scale industrial benchmarks.

References

[1]

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 TCAD, vol. 33, no. 11, pp. 1671--1680, 2014.

[2]

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 Proc. DAC, 2012, pp. 1167--1172.

Digital Library

[3]

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 TCAD, vol. 34, no. 3, pp. 460--470, 2015.

[4]

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.

[5]

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

[6]

A. J. Torres, "ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite," in Proc. ICCAD, 2012, pp. 349--350.

Digital Library

[7]

T. Matsunawa, B. Yu, and D. Z. Pan, "Optical proximity correction with hierarchical bayes model," in Proc. SPIE, vol. 9426, 2015.

[8]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. NIPS, 2012, pp. 1097--1105.

Digital Library

[9]

T. Xiao, H. Li, W. Ouyang, and X. Wang, "Learning deep feature representations with domain guided dropout for person re-identification," in Proc. CVPR, 2016, pp. 1249--1258.

[10]

W. Zhang, X. Li, S. Saxena, A. Strojwas, and R. Rutenbar, "Automatic clustering of wafer spatial signatures," in Proc. DAC, 2013, pp. 71:1--71:6.

Digital Library

[11]

S. Shim, W. Chung, and Y. Shin, "Synthesis of lithography test patterns through topology-oriented pattern extraction and classification," in Proc. SPIE, vol. 9053, 2014.

[12]

G. K. Wallace, "The JPEG still picture compression standard," IEEE TCE, vol. 38, no. 1, pp. xviii--xxxiv, 1992.

Digital Library

[13]

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 6088, pp. 533--536, 1986.

[14]

W. A. Gardner, "Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique," Signal Processing, vol. 6, no. 2, pp. 113--133, 1984.

[15]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, http://www.deeplearningbook.org.

Digital Library

[16]

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

[17]

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

[18]

S. Hochreiter, "The vanishing gradient problem during learning recurrent neural nets and problem solutions," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 02, pp. 107--116, 1998.

Digital Library

Cited By

Wang CFang YZhang S(2024)Feature Fusion based Hotspot Detection with R-EfficientNetProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658707(446-451)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658707
Kim SJeon J(2024)Methodology for Lithography Hotspot Detection using ResNet50V2 and Model soups2024 International Conference on Electronics, Information, and Communication (ICEIC)10.1109/ICEIC61013.2024.10457195(1-4)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICEIC61013.2024.10457195
Shahroz MAli MTahir AFabian Gongora HUc Rios CAbdus Samad MAshraf I(2024)Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network ModelIEEE Access10.1109/ACCESS.2024.342261612(92840-92855)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422616
Show More Cited By

Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

Recommendations

Hotspot detection via attention-based deep layout metric learning
ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design

With the aggressive and amazing scaling of the feature size of semiconductors, hotspot detection has become a crucial and challenging problem in the generation of optimized mask design for better printability. Machine learning techniques, especially ...
Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

Detecting layout hotspots is a key step in the physical verification flow. Although machine learning solutions show benefits over lithography simulation and pattern matching-based methods, it is still hard to select a proper model for large scale ...
Efficient Layout Hotspot Detection via Binarized Residual Neural Network
DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great successes. The layouts can be viewed as binary images. The binarized neural network ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

June 2017

533 pages

ISBN:9781450349277

DOI:10.1145/3061639

Copyright © 2017 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

EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

DAC '17

Sponsor:

EDAC
SIGDA

DAC '17: The 54th Annual Design Automation Conference 2017

June 18 - 22, 2017

TX, Austin, USA

Acceptance Rates

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

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
460
Total Downloads

Downloads (Last 12 months)88
Downloads (Last 6 weeks)20

Reflects downloads up to 17 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang CFang YZhang S(2024)Feature Fusion based Hotspot Detection with R-EfficientNetProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658707(446-451)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658707
Kim SJeon J(2024)Methodology for Lithography Hotspot Detection using ResNet50V2 and Model soups2024 International Conference on Electronics, Information, and Communication (ICEIC)10.1109/ICEIC61013.2024.10457195(1-4)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICEIC61013.2024.10457195
Shahroz MAli MTahir AFabian Gongora HUc Rios CAbdus Samad MAshraf I(2024)Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network ModelIEEE Access10.1109/ACCESS.2024.342261612(92840-92855)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3422616
Chen TXiong SHe HYu B(2023)TRouter: Thermal-Driven PCB Routing via Nonlocal Crisscross Attention NetworksIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.324354442:10(3388-3401)Online publication date: Oct-2023
https://doi.org/10.1109/TCAD.2023.3243544
Pei ZZhao WHe ZYu B(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
https://doi.org/10.1109/ISEDA59274.2023.10218502
Chockalingam ANaveen SSanjay SNanthakumar JPraveenkumar V(2023)Sensor Based Hotspot Detection And Isolation In Solar Array System Using IOT2023 9th International Conference on Electrical Energy Systems (ICEES)10.1109/ICEES57979.2023.10110240(371-376)Online publication date: 23-Mar-2023
https://doi.org/10.1109/ICEES57979.2023.10110240
Cui JZhang JWang X(2023)Lithographic Hotspot Detection Using Adaptive Squish Pattern Sampling Combined with Faster R-CNN2023 IEEE 15th International Conference on ASIC (ASICON)10.1109/ASICON58565.2023.10396633(1-4)Online publication date: 24-Oct-2023
https://doi.org/10.1109/ASICON58565.2023.10396633
Gai TQu TWang SSu XXu RWang YXue JSu YWei YYe T(2022)Flexible Hotspot Detection Based on Fully Convolutional Network With Transfer LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.313578641:11(4626-4638)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2021.3135786
Yang YSun M(2022)Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00236(2313-2322)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00236
Geng HChen TSun QYu B(2022)Techniques for CAD Tool Parameter Auto-tuning in Physical Synthesis: A Survey (Invited Paper)2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC52403.2022.9712495(635-640)Online publication date: 17-Jan-2022
https://doi.org/10.1109/ASP-DAC52403.2022.9712495
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