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MOSAIC: Mask Optimizing Solution With Process Window Aware Inverse Correction

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Published:01 June 2014Publication History

ABSTRACT

Optical Proximity Correction (OPC) has been widely adopted for resolution enhancement to achieve nanolithography. However, conventional rule-based and model-based OPCs encounter severe difficulties at advanced technology nodes. Inverse Lithography Technique (ILT) that solves the inverse problem of the imaging system becomes a promising solution for OPC. In this paper, we consider simultaneously 1) the design target optimization under nominal process condition and 2) process window minimization with different process corners, and solve the mask optimization problem based on ILT. The proposed method is tested on 32nm designs released by IBM for the ICCAD 2013 contest. Our optimization is implemented in two modes, MOSAIC_fast and MOSAIC_exact, which outperform the first place winner of the ICCAD 2013 contest by 7% and 11%, respectively.

References

  1. A. K. Wong. Resolution enhancement techniques. SPIE Press, 2001.Google ScholarGoogle Scholar
  2. N. B. Cobb and Y. Granik. OPC methods to improve image slope and process window. In Proc. of SPIE, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  3. P. Yu and D. Z. Pan. TIP-OPC: a new topological invariant paradigm for pixel based optical proximity correction. In Proc. Int. Conf. on Computer Aided Design, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Y. Granik. Illuminator optimization methods in microlithography. In Proc. of SPIE, volume 5754, 2005.Google ScholarGoogle Scholar
  5. L. Pang, Y. Liu, and D. Abrams. Inverse lithography technology (ILT): What is the impact to the photomask industry? In Proc. of SPIE, volume 6283, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  6. Guangming Xiao, Dong Hwan Son, Tom Cecil, Dave Irby, David Kim, Ki-Ho Baik, Byung-Gook Kim, SungGon Jung, Sung Soo Suh, and HanKu Cho. E-beam writing time improvement for inverse lithography technology mask for full-chip. In Proc. of SPIE, volume 7748, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  7. Y. Granik. Fast pixel-based mask optimization for inverse lithography. Journal of Micro/Nanolithography, MEMS, and MOEMS, 5(4), 2006.Google ScholarGoogle Scholar
  8. Y. Shen, N. Wong, and E. Y. Lam. Level-set-based inverse lithography for photomask synthesis. Optics Express, 17(26), 2009.Google ScholarGoogle Scholar
  9. A. Poonawala and P. Milanfar. Mask design for optical microlithography mdash;An inverse imaging problem. IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, 16(3), 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. X. Ma and G. R. Arce. Generalized inverse lithography methods for phase-shifting mask design. In Optics Express, volume 15, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  11. N. Jia and E. Y. Lam. Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis. Journal of Optics, 12(4), 2010.Google ScholarGoogle ScholarCross RefCross Ref
  12. X. Zhao and C. Chu. Line search-based inverse lithography technique for mask design. Proc. Int. Conf. on VLSI Design, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jinyu Zhang, Wei Xiong, Yan Wang, and Zhiping Yu. A highly efficient optimization algorithm for pixel manipulation in inverse lithography technique. In Proc. Int. Conf. on Computer Aided Design, November 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jinyu Zhang, Wei Xiong, Yan Wang, Zhiping Yu, and Min-Chun Tsai. A robust pixel-based RET optimization algorithm independent of initial conditions. In Proc. Asia and South Pacific Design Automation Conf., January 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Krasnoperova, J. A. Culp, I. Graur, S. Mansfield, M. Al-Imam, and H. Maaty. Process window OPC for reduced process variability and enhanced yield. In Proc. of SPIE, volume 6154, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  16. P.Yu, S.X. Shi, and D. Z. Pan. True process variation aware optical proximity correction with variational lithography modeling and model calibration. Journal of Micro/Nanolithography, MEMS, and MOEMS, 6(3), 2007.Google ScholarGoogle ScholarCross RefCross Ref
  17. S. Banerjee, K. B. Agarwal, and M. Orshansky. Methods for joint optimization of mask and design targets for improving lithographic process window. Journal of Micro/Nanolithography, MEMS, and MOEMS, 12(2), 2013.Google ScholarGoogle ScholarCross RefCross Ref
  18. H. Hopkins. The concept of partial coherence in optics. Proceedings of the Royal Society of London, 1953.Google ScholarGoogle Scholar
  19. N. Cobb. Fast optical and process proximity correction algorithms for integrated circuit manufacturing. PhD dissertation, University of California at Berkeley, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. A. Torres and C. N. Berglund. Integrated circuit DFM framework for deep subwavelength processes. In Proc. of SPIE, volume 5756, 2005.Google ScholarGoogle Scholar
  21. X. Ma and G. R. Arce. Computational Lithography. Wiley Series in Pure and Applied Optics, first edition, 2010.Google ScholarGoogle Scholar
  22. ICCAD contest 2013 {http://cad_contest.cs.nctu.edu.tw/CAD-contest-at-ICCAD2013/problem_c/}.Google ScholarGoogle Scholar
  1. MOSAIC: Mask Optimizing Solution With Process Window Aware Inverse Correction

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    • Published in

      cover image ACM Other conferences
      DAC '14: Proceedings of the 51st Annual Design Automation Conference
      June 2014
      1249 pages
      ISBN:9781450327305
      DOI:10.1145/2593069

      Copyright © 2014 ACM

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      Publication History

      • Published: 1 June 2014

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