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A novel true random number generator based on a stochastic diffusive memristor

Author

Listed:
  • Hao Jiang

    (University of Massachusetts)

  • Daniel Belkin

    (University of Massachusetts
    Swarthmore College)

  • Sergey E. Savel’ev

    (Loughborough University)

  • Siyan Lin

    (University of Massachusetts)

  • Zhongrui Wang

    (University of Massachusetts)

  • Yunning Li

    (University of Massachusetts)

  • Saumil Joshi

    (University of Massachusetts)

  • Rivu Midya

    (University of Massachusetts)

  • Can Li

    (University of Massachusetts)

  • Mingyi Rao

    (University of Massachusetts)

  • Mark Barnell

    (Information Directorate)

  • Qing Wu

    (Information Directorate)

  • J. Joshua Yang

    (University of Massachusetts)

  • Qiangfei Xia

    (University of Massachusetts)

Abstract

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things.

Suggested Citation

  • Hao Jiang & Daniel Belkin & Sergey E. Savel’ev & Siyan Lin & Zhongrui Wang & Yunning Li & Saumil Joshi & Rivu Midya & Can Li & Mingyi Rao & Mark Barnell & Qing Wu & J. Joshua Yang & Qiangfei Xia, 2017. "A novel true random number generator based on a stochastic diffusive memristor," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00869-x
    DOI: 10.1038/s41467-017-00869-x
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    Cited by:

    1. Haider, Muhammad Imran & Shah, Tariq & Ali, Asif & Shah, Dawood & Khalid, Ijaz, 2023. "An Innovative approach towards image encryption by using novel PRNs and S-boxes Modeling techniques," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 153-168.
    2. Kyung Seok Woo & Jaehyun Kim & Janguk Han & Woohyun Kim & Yoon Ho Jang & Cheol Seong Hwang, 2022. "Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Jinshi Li & Pingchuan Shen & Zeyan Zhuang & Junqi Wu & Ben Zhong Tang & Zujin Zhao, 2023. "In-situ electro-responsive through-space coupling enabling foldamers as volatile memory elements," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Kyung Seok Woo & Alan Zhang & Allison Arabelo & Timothy D. Brown & Minseong Park & A. Alec Talin & Elliot J. Fuller & Ravindra Singh Bisht & Xiaofeng Qian & Raymundo Arroyave & Shriram Ramanathan & Lu, 2024. "True random number generation using the spin crossover in LaCoO3," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    5. Seou Choi & Yannick Salamin & Charles Roques-Carmes & Rumen Dangovski & Di Luo & Zhuo Chen & Michael Horodynski & Jamison Sloan & Shiekh Zia Uddin & Marin Soljačić, 2024. "Photonic probabilistic machine learning using quantum vacuum noise," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    6. Akhil Dodda & Nicholas Trainor & Joan. M. Redwing & Saptarshi Das, 2022. "All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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