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True random number generation using the spin crossover in LaCoO3

Author

Listed:
  • Kyung Seok Woo

    (Sandia National Laboratories
    Texas A&M University
    Lawrence Berkeley National Laboratory)

  • Alan Zhang

    (Sandia National Laboratories)

  • Allison Arabelo

    (Texas A&M University)

  • Timothy D. Brown

    (Sandia National Laboratories)

  • Minseong Park

    (Sandia National Laboratories
    Texas A&M University)

  • A. Alec Talin

    (Sandia National Laboratories)

  • Elliot J. Fuller

    (Sandia National Laboratories)

  • Ravindra Singh Bisht

    (The State University of New Jersey)

  • Xiaofeng Qian

    (Texas A&M University)

  • Raymundo Arroyave

    (Texas A&M University)

  • Shriram Ramanathan

    (The State University of New Jersey)

  • Luke Thomas

    (Applied Materials Inc.)

  • R. Stanley Williams

    (Sandia National Laboratories
    Texas A&M University)

  • Suhas Kumar

    (Sandia National Laboratories)

Abstract

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49149-5
    DOI: 10.1038/s41467-024-49149-5
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    References listed on IDEAS

    as
    1. 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.
    2. Gwangmin Kim & Jae Hyun In & Young Seok Kim & Hakseung Rhee & Woojoon Park & Hanchan Song & Juseong Park & Kyung Min Kim, 2021. "Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Suhas Kumar & R. Stanley Williams & Ziwen Wang, 2020. "Third-order nanocircuit elements for neuromorphic engineering," Nature, Nature, vol. 585(7826), pages 518-523, September.
    4. Suhas Kumar & John Paul Strachan & R. Stanley Williams, 2017. "Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing," Nature, Nature, vol. 548(7667), pages 318-321, August.
    5. Kyung Seok Woo & Janguk Han & Su-in Yi & Luke Thomas & Hyungjun Park & Suhas Kumar & Cheol Seong Hwang, 2024. "Tunable stochastic memristors for energy-efficient encryption and computing," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Peng Yao & Huaqiang Wu & Bin Gao & Jianshi Tang & Qingtian Zhang & Wenqiang Zhang & J. Joshua Yang & He Qian, 2020. "Fully hardware-implemented memristor convolutional neural network," Nature, Nature, vol. 577(7792), pages 641-646, January.
    7. 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.
    8. Suhas Kumar & Ziwen Wang & Noraica Davila & Niru Kumari & Kate J. Norris & Xiaopeng Huang & John Paul Strachan & David Vine & A.L. David Kilcoyne & Yoshio Nishi & R. Stanley Williams, 2017. "Physical origins of current and temperature controlled negative differential resistances in NbO2," Nature Communications, Nature, vol. 8(1), pages 1-6, December.
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