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Tunable stochastic memristors for energy-efficient encryption and computing

Author

Listed:
  • Kyung Seok Woo

    (Seoul National University
    Sandia National Laboratories
    Texas A&M University)

  • Janguk Han

    (Seoul National University)

  • Su-in Yi

    (Texas A&M University)

  • Luke Thomas

    (Applied Materials Inc.)

  • Hyungjun Park

    (Seoul National University)

  • Suhas Kumar

    (Sandia National Laboratories)

  • Cheol Seong Hwang

    (Seoul National University)

Abstract

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements – security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 (‘CuTeHO’) ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47488-x
    DOI: 10.1038/s41467-024-47488-x
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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. Julien Borghetti & Gregory S. Snider & Philip J. Kuekes & J. Joshua Yang & Duncan R. Stewart & R. Stanley Williams, 2010. "‘Memristive’ switches enable ‘stateful’ logic operations via material implication," Nature, Nature, vol. 464(7290), pages 873-876, April.
    4. Jung Ho Yoon & Zhongrui Wang & Kyung Min Kim & Huaqiang Wu & Vignesh Ravichandran & Qiangfei Xia & Cheol Seong Hwang & J. Joshua Yang, 2018. "An artificial nociceptor based on a diffusive memristor," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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