International Journal of Fuzzy Logic and Intelligent Systems

  • 제16권3호
  • /
  • Pages.208-215
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  • 2016
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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CNN Based Lithography Hotspot Detection

  • Shin, Moojoon (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Lee, Jee-Hyong (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • 투고 : 2016.09.07
  • 심사 : 2016.09.14
  • 발행 : 2016.09.25
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초록

The lithography hotspot detection process is crucial for semiconductor design development process. But, the lithography hotspot detection using optical simulation method takes much time and it slowdown the layout design development cycle. Though the geometry based approach is introduced as an alternative, it still revealed low detection performance and sophisticated framework. To solve this problem, we introduce a deep convolutional neural network based hotspot detection method. Our method made better results in ICCCAD 2012 dataset. To reach this score, we used lots of technical effort to improve the result in addition to just utilizing the nature of convolutional neural network. Inspection region reduction, data augmentation, DBSCAN clustering helped our work more stable and faster.

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참고문헌

  1. H. Yao, S. Sinha, C. Chiang, X. Hong, and Y. Cai, "Efficient process-hotspot detection using range pattern matching," in Proceedings of the 2006 IEEE/ACM International Conference on Computer-Aided Design, San Jose, CA, 2006, pp. 625-632. http://dx.doi.org/10.1145/1233501.1233630
  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 Proceedings of the 49th Annual Design Automation Conference, San Francisco, CA, 2012, pp. 1167-1172. http://dx.doi.org/10.1145/2228360.2228576
  3. A. B. Kahng, C. H. Park, and X. Xu, "Fast dual-graphbased hotspot filtering," IEEE Transactions on Computer- Aided Design of Integrated Circuits and Systems, vol. 27, no. 9, pp. 1635-1642, 2008. http://dx.doi.org/10.1109/TCAD.2008.927765
  4. B. Yu, J. R. Gao, D. Ding, X. Zeng, and D. Z. Pan, "Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering," Journal of Micro/ Nanolithography, MEMS, and MOEMS, vol. 14, no. 1, pp. 1-12, 2014. http://dx.doi.org/10.1117/1.JMM.14.1.011003
  5. 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 Proceedings of the 50th Annual Design Automation Conference, Austin, TX, 2013, pp. 671-676. http://dx.doi.org/10.1145/2463209.2488816
  6. J. Y. Wuu, F. G. Pikus, A. Torres, and M. Marek-Sadowska, "Rapid layout pattern classification," in Proceedings of the 2011 16th Asia and South Pacific Design Automation Conference, Yokohama, Japan, 2011, pp. 781-786. http://dx.doi.org/10.1109/ASPDAC.2011.5722295
  7. 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 Society of Photo-Optical Instrumentation Engineers (vol. 9427), San Jose, CA, 2015, pp. 1-11. http://dx.doi.org/10.1117/12.2085790
  8. J. A. Torres, "ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite," in Proceedings of 2012 International Conference on Computer-Aided Design, San Jose, CA, 2012, pp. 349-350. http://dx.doi.org/10.1145/2429384.2429457
  9. D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks," in Proceedings of 2013 Medical Image Computing and Computer- Assisted Intervention, Nagoya, Japan, 2013, pp. 411-418. http://dx.doi.org/10.1007/978-3-642-40763-5 51
  10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proceedings of 2012 Neural Information Processing Systems, Lake Tahoe, Canada, 2012, pp. 1-9.
  11. M. Ester, H. P. Kriegel, J. Sander, and X. Xu, "A densitybased algorithm for discovering clusters in large spatial databases with noise," in Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226-231.
  12. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, "Theano: a CPU and GPU math compiler in Python," in Proceedings of the 9th Python in Science Conference, Austin, TX, 2010, pp. 1-7.

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