skip to main content
10.1145/2742060.2742088acmconferencesArticle/Chapter ViewAbstractPublication PagesglsvlsiConference Proceedingsconference-collections
short-paper

A Novel True Random Number Generator Design Leveraging Emerging Memristor Technology

Authors Info & Claims
Published:20 May 2015Publication History

ABSTRACT

Memristor, the fourth basic circuit element, demonstrates obvious stochastic behaviors in both the static resistance states and the dynamic switching. In this work, a novel memristor-based true random number generator (MTRNG) is presented which leverages the stochastic property when switching a device between its binary states. Compared to conventional random number generators that require amplifiers or comparators with high complexity, the use of memristors significantly reduces the design cost: a basic MTRNG consists of only one memristor, six transistors, and one D Flip-flop. To maximize the entropy of the random bit generation, we further enhanced the design to a 2-branch scheme which can provide a uniform bit distribution. Our simulation results show that the proposed MTRNGs offer high operating speed and low power consumption: the reading clocks of the basic 1-branch and the enhanced 2-branch schemes can reach at 1.05GHz and 0.96GHz with power assumptions of 31.1"W and 80.3"W, respectively. Moreover, the zero-versus-one distributions and sampling rates of MTRNGs can be flexibly reconfigured by modulating the width and amplitude of the programming pulse applied on a memristor and therefore adjusting its switching probability between ON and OFF states.

References

  1. T. L. Blackwell, "Applications of randomness in system performance measurement," Citeseer, 1998.Google ScholarGoogle Scholar
  2. R. S. DeBellis, R. M. Smith Sr, and P. C.-C. Yeh, "Pseudo random number generator," US Patents US6061703, 2000.Google ScholarGoogle Scholar
  3. S. Fujita, K. Uchida, S. Yasuda, R. Ohba, H. Nozaki, and T. Tanamoto, "Si nanodevices for random number generating circuits for cryptographic security," in 2004 IEEE International Solid-State Circuits Conference (ISSCC), 2004, pp. 294--295.Google ScholarGoogle Scholar
  4. C.-Y. Huang, W. C. Shen, Y.-H. Tseng, Y.-C. King, and C.-J. Lin, "A contact-resistive random-access-memory-based true random number generator," IEEE Electron Device Letters, vol. 33, pp. 1108--1110, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  5. V. C. Vivoli, P. Sekatski, J. D. Bancal, C. C. W. Lim, A. Martin, R. T. Thew, et al., "Device-independent quantum random number generator with a photon pair source," arXiv preprint arXiv:1409.8051, 2014.Google ScholarGoogle Scholar
  6. A. Fukushima, T. Seki, K. Yakushiji, H. Kubota, H. Imamura, S. Yuasa, et al., "Spin dice: A scalable truly random number generator based on spintronics," Applied Physics Express, vol. 7, p. 083001, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  7. Y. Yang and W. Lu, "Nanoscale resistive switching devices: mechanisms and modeling," Nanoscale, vol. 5, pp. 10076--10092, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Gaba, P. Knag, Z. Zhang, and W. Lu, "Memristive devices for stochastic computing," in 2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014, pp. 2592--2595.Google ScholarGoogle Scholar
  9. S. Kim, S. Choi, and W. Lu, "Comprehensive Physical Model of Dynamic Resistive Switching in an Oxide Memristor," ACS nano, vol. 8, pp. 2369--2376, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. L. Chua, "Resistance switching memories are memristors," Applied Physics A, vol. 102, pp. 765--783, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Hu, H. Li, Y. Chen, Q. Wu, G. Rose, and W. Linderman, "Memristor Crossbar Based Neuromorphic Computing System: A Case Study," IEEE Transactions on Neural Network and Learning System (TNNLS), vol. 25, no 10, pp. 1864--1878, Oct. 2014.Google ScholarGoogle ScholarCross RefCross Ref
  12. A. F. Vincent, J. Larroque, W. S. Zhao, N. B. Romdhane, O. Bichler, C. Gamrat, et al., "Spin-transfer torque magnetic memory as a stochastic memristive synapse," in Circuits and Systems (ISCAS), 2014 IEEE International Symposium on, 2014, pp. 1074--1077.Google ScholarGoogle Scholar
  13. S. Gaba, P. Sheridan, J. Zhou, S. Choi, and W. Lu, "Stochastic memristive devices for computing and neuromorphic applications," Nanoscale, vol. 5, pp. 5872--5878, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  14. M.-J. Lee, C. B. Lee, D. Lee, S. R. Lee, M. Chang, J. H. Hur, et al., "A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x/TaO2" x bilayer structures," Nature materials, vol. 10, pp. 625--630, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  15. A. C. Torrezan, J. P. Strachan, G. Medeiros-Ribeiro, and R. S. Williams, "Subnanosecond switching of a tantalum oxide memristor," Nanotechnology, vol. 22, p. 485203, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  16. L. O. Chua, "Memristor-the missing circuit element," IEEE Transactions on Circuit Theory, vol. 18, pp. 507--519, 1971.Google ScholarGoogle ScholarCross RefCross Ref
  17. R. Williams, "How we found the missing memristor," IEEE Spectrum, vol. 45, no. 12, pp. 28--35, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Hu, Y. Wang, Q. Qiu, Y. Chen, and H. Li, "The stochastic modeling of TiO2 memristor and its usage in neuromorphic system design," in Asia and South Pacific Design Automation Conference (ASP-DAC), 2014, pp. 831--836.Google ScholarGoogle Scholar
  19. S. Yu, B. Gao, Z. Fang, H. Yu, J. Kang, and H.-S. P. Wong, "Stochastic learning in oxide binary synaptic device for neuromorphic computing," Frontiers in neuroscience, vol. 7, 2013.Google ScholarGoogle Scholar
  20. G. Medeiros-Ribeiro, F. Perner, R. Carter, H. Abdalla, M. D. Pickett, and R. S. Williams, "Lognormal switching times for titanium dioxide bipolar memristors: origin and resolution," Nanotechnology, vol. 22, p. 095702, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  21. W. Yi, et al., "Feedback write scheme for memristive switching devices," Appl. Phys. A, vol. 102, pp. 973--982, 2011.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. A Novel True Random Number Generator Design Leveraging Emerging Memristor Technology

    Recommendations

    Comments

    Login options

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

    Sign in
    • Published in

      cover image ACM Conferences
      GLSVLSI '15: Proceedings of the 25th edition on Great Lakes Symposium on VLSI
      May 2015
      418 pages
      ISBN:9781450334747
      DOI:10.1145/2742060

      Copyright © 2015 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 20 May 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      GLSVLSI '15 Paper Acceptance Rate41of148submissions,28%Overall Acceptance Rate312of1,156submissions,27%

      Upcoming Conference

      GLSVLSI '24
      Great Lakes Symposium on VLSI 2024
      June 12 - 14, 2024
      Clearwater , FL , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader