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Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

Author

Listed:
  • Kyung Seok Woo

    (Seoul National University)

  • Jaehyun Kim

    (Seoul National University)

  • Janguk Han

    (Seoul National University)

  • Woohyun Kim

    (Seoul National University)

  • Yoon Ho Jang

    (Seoul National University)

  • Cheol Seong Hwang

    (Seoul National University)

Abstract

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33455-x
    DOI: 10.1038/s41467-022-33455-x
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    References listed on IDEAS

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    Cited by:

    1. Nihal Sanjay Singh & Keito Kobayashi & Qixuan Cao & Kemal Selcuk & Tianrui Hu & Shaila Niazi & Navid Anjum Aadit & Shun Kanai & Hideo Ohno & Shunsuke Fukami & Kerem Y. Camsari, 2024. "CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Hakseung Rhee & Gwangmin Kim & Hanchan Song & Woojoon Park & Do Hoon Kim & Jae Hyun In & Younghyun Lee & Kyung Min Kim, 2023. "Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    3. 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.
    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. ZhuangEn Fu & Piumi I. Samarawickrama & John Ackerman & Yanglin Zhu & Zhiqiang Mao & Kenji Watanabe & Takashi Taniguchi & Wenyong Wang & Yuri Dahnovsky & Mingzhong Wu & TeYu Chien & Jinke Tang & Allan, 2024. "Tunneling current-controlled spin states in few-layer van der Waals magnets," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. 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.

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