Table of Contents Author Guidelines Submit a Manuscript
VLSI Design
Volume 2013 (2013), Article ID 984376, 22 pages
http://dx.doi.org/10.1155/2013/984376
Research Article

Computational Performance Optimisation for Statistical Analysis of the Effect of Nano-CMOS Variability on Integrated Circuits

School of Computer Science, The University of Manchester, Manchester M13 9PL, UK

Received 16 November 2012; Revised 15 May 2013; Accepted 15 May 2013

Academic Editor: Chien-In Henry Chen

Copyright © 2013 Zheng Xie and Doug Edwards. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Onodera, “Variability: modeling and its impact on design,” IEICE Transactions on Electronics, vol. E89-C, no. 3, pp. 342–348, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Asenov, A. R. Brown, J. H. Davies, S. Kaya, and G. Slavcheva, “Simulation of intrinsic parameter fluctuations in decananometer and nanometer-scale MOSFETs,” IEEE Transactions on Electron Devices, vol. 50, no. 9, pp. 1837–1852, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. G. Roy, A. R. Brown, F. Adamu-Lema, S. Roy, and A. Asenov, “Simulation study of individual and combined sources of intrinsic parameter fluctuations in conventional nano-MOSFETs,” IEEE Transactions on Electron Devices, vol. 53, no. 12, pp. 3063–3069, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Srivastava, D. Sylvester, and D. Blaauw, Statistical Analysis and Optimization for VLSI: Timing and Power, Springer, Berlin, Germany, 2005.
  5. B. Hargreaves, H. Hult, and S. Reda, “Within-die process variations: How accurately can they be statistically modeled?” in Proceedings of Asia and South Pacific Design Automation Conference (ASP-DAC '08), pp. 524–530, March 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Singhee and R. A. Rutenbar, “Statistical blockade: a novel method for very fast Monte Carlo simulation of rare circuit events, and its application,” in Proceedings of Design, Automation and Test in Europe Conference and Exhibition, pp. 1379–1384, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Singhee, J. Wang, B. H. Calhoun, and R. A. Rutenbar, “Recursive statistical blockade: an enhanced technique for rare event simulation with application to SRAM circuit design,” in Proceedings of the 21st International Conference on VLSI Design, pp. 131–136, January 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Xie, Computation reduction for statistical analysis of the effect of nano-CMOS variability on integrated circuits [Ph.D. thesis], The University of Manchester, Manchester, UK, 2012.
  9. Z. Xie and D. Edwards, “Computation reduction for statistical analysis of the effect of nano-CMOS variability on asynchronous circuits,” in Proceedings of the 13th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '10), pp. 161–166, Vienna, Austria, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. E. J. Gumbel, Statistical Theory of Extreme Values and Some Practical Applications, vol. 33 of Applied Mathematical, US Department of Commerce, National Bureau of Standards, Washington, DC, USA, 1954.
  11. D. Thain, T. Tannenbaun, and M. Livny, “Distributed computing in practice: the condor experience,” Concurrency and Computation, vol. 17, no. 2–4, pp. 323–356, 2005. View at Google Scholar
  12. “High Throughput Computing Using Condor at Manchester,” October 2011, http://condor.eps.manchester.ac.uk/.
  13. N. Metropolis and S. Ulam, “The Monte Carlo method,” Journal of the American Statistical Association, vol. 44, no. 247, pp. 335–341, 1949. View at Google Scholar · View at Scopus
  14. “NGSPICE release 23,” June 2011, http://www.sourceforge.net,.
  15. G. Peter Lepage, “A new algorithm for adaptive multidimensional integration,” Journal of Computational Physics, vol. 27, no. 2, pp. 192–203, 1978. View at Google Scholar · View at Scopus
  16. H. Niederreiter, " Random Number Generation and Quasi-Monte Carlo Methods, vol. 63 of Regional Conference Series in Applied Mathematics, SIAM, Philadelphia, Pa, USA, 1992.
  17. R. Bellman, Adaptive Control Processes: A Guided Tour, Princeton University Press, Princeton, NJ, USA, 1961.
  18. C. Lemieux, Monte Carlo and Quasi-Monte Carlo Sampling, Springer Series in Statistics, Springer, New York, NY, USA, 2009.
  19. H. Niederreiter, “Quasi-Monte Carlo methods and pseudo-random numbers,” Bulletin of the American Mathematical Society, 1978. View at Google Scholar
  20. S. K. Chaudhary, Acceleration of Monte Carlo methods using low discrepancy sequences [Ph.D. thesis], UCLA, Los Angeles, Calif, USA, 2004.
  21. D. I. Asotsky, E. E. Myshetskaya, and I. M. Sobol', “The average dimension of a multidimensional function for quasi-Monte Carlo estimates of an integral,” Computational Mathematics and Mathematical Physics, vol. 46, no. 12, pp. 2061–2067, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. A. B. Owen, “Scrambled net variance for integrals of smooth functions,” Annals of Statistics, vol. 25, no. 4, pp. 1541–1562, 1997. View at Google Scholar · View at Scopus
  23. W. H. Press and G. R. Farrar, “Recursive stratified sampling for multidimensional Monte Carlo integration,” Computers in Physics, vol. 4, pp. 190–195, 1990. View at Google Scholar
  24. R. Schürer, “Adaptive Quasi-Monte Carlo integration based on MISER and VEGAS,” in Monte Carlo and Quasi-Monte Carlo Methods, pp. 393–406, Springer, Berlin, Germany, 2002. View at Google Scholar
  25. G. P. Lepage, “A new algorithm for adaptive multidimensional integration,” Journal of Computational Physics, vol. 27, pp. 192–203, 1978. View at Google Scholar
  26. G. P. Lepage, “VEGAS: an adaptive multi-dimensional integration program,” Cornell preprint CLNS 80-447, March 1980. View at Google Scholar
  27. J. Keiner and U. Waterhouse, “Fast principal components analysis method for finance problems with unequal time steps,” in Monte Carlo and Quasi-Monte Carlo Methods 2008, P. L'Ecuyer and A. B. Owen, Eds., Springer, New York, NY, USA, 2010. View at Google Scholar
  28. “RandomSPICE”-info@GoldStandardSimulations.comhttp://www.GoldStandardSimulations.com/services/circuit-simulation/random-spice/, 2013.
  29. B. Bindu, B. Cheng, G. Roy, X. Wang, S. Roy, and A. Asenov, “Parameter set and data sampling strategy for accurate yet efficient statistical MOSFET compact model extraction,” Solid-State Electronics, vol. 54, no. 3, pp. 307–315, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. HSPICE User Guide: Simulation and Analysis, Version A-2007, Synopsys, Mountain View, Calif, USA, 2007.
  31. J. P. Fishburn and A. E. Dunlop, “TILOS: a polynomial programming approach to transistor sizing,” in Proceedings of IEEE International Conference on Computer-Aided Design (ICCAD '85), pp. 326–328, November 1985.
  32. D. P. Marple and A. El Gamal, “Optimal selection of transistor sizes in digital VLSI,” in Proceedings of the Stanford Conference on Advanced Research in VLSI, pp. 151–172, MIT Press, 1987.
  33. W. Obermeier, An open architecture for improving VLSI circuit performance [Ph.D. thesis], University of California, Berkekey, Calif, USA, 1989.
  34. S. M. Burns, Performance analysis and optimization of Asynchronous Circuits [Ph.D. thesis], California Institute of Technology, Pasadena, Calif, USA, 1990.
  35. G. Mekhtarian, Composite Current Source (CCS) Modeling Technology Backgrounder, Synopsys, Mountain View, Calif, USA, 2005, . 11/05.KF.WO .05-13816.
  36. R. Goyal and N. Kumar, Current Based Delay Models: A Must for Nanometer Timing, Cadence Design Systems, Noida, India, 2005.
  37. Synopsys, “Liberty Library Modeling,” July 2012, http://www.synopsys.com/community/interoperability/pages/libertylibmodel.aspx.
  38. J. F. Croix and D. F. Wong, “Blade and Razor: cell and interconnect delay analysis using current-based models,” in Proceedings of the 40th Design Automation Conference, pp. 386–389, June 2003. View at Scopus
  39. J. Li, H. Zhao, and H.-Y. Chiu, “Accuracy Timing Models for Integrated Circuit Verification,” U.S. patent number 6721929, April 2004.
  40. D. Dasx, W. Scotty, S. Nazariany, and H. Zhoux, “An efficient current-based logic cell model for crosstalk delay analysis,” in Proceedings of the 10th International Symposium on Quality Electronic Design (ISQED '09), pp. 627–633, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Rewieński and J. White, “A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 22, no. 2, pp. 155–170, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. P. Li, Z. Feng, and E. Acar, “Characterizing multistage nonlinear drivers and variability for accurate timing and noise analysis,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 15, no. 11, pp. 1205–1214, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. C. Visweswariah, K. Ravindran, K. Kalafala et al., “First-order incremental block-based statistical timing analysis,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 25, no. 10, pp. 2170–2179, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. S. I. Resnick, Extreme Values, Regular Variation and Point Processes, Springer, New York, Ny, USA, 1987.
  45. D. Edwards, A. Bardsley, L. Janin, and W. Toms, Balsa: A Tutorial Guide, School of Computer Science, University of Manchester, Manchester, UK, 2008, http://apt.cs.man.ac.uk/projects/tools/balsa/.
  46. J. Sparsø and S. Furber, Principles of Asynchronous Circuit Design, Kluwer Academic Publishers, Norwell, Mass, USA, 2005.
  47. A. Singhee, Novel Algorithms for Fast Statistical Analysis of Scaled Circuits [Ph.D. thesis], Carnegie Mellon University, Pittsburgh, Pa, USA, 2007.
  48. R. E. Caflisch, “Monte Carlo and quasi-Monte Carlo methods,” Acta Numerica, vol. 7, pp. 1–49, 1998. View at Google Scholar
  49. L. Kuipers and H. Niederreiter, Uniform Distribution of Sequences, Dover Publications, Dover, UK, 2005.
  50. J. Spanier, “Quasi-Monte Carlo methods for particle transport problems,” in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, H. Niederreiter and P. J. -S. Shiue, Eds., pp. 121–148, Springer, New York, NY, USA, 1995. View at Google Scholar
  51. E. J. Gumbel, “Statistical Theory of Extreme Values and Some Practical Applications, vol. 33 of Applied Mathematical, Series, US Department of Commerce, National Bureau of Standards, Washington, DC, USA, 1954.