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Self-rectifying resistive memory in passive crossbar arrays

Author

Listed:
  • Kanghyeok Jeon

    (Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu
    Hanyang University)

  • Jeeson Kim

    (Hanyang University)

  • Jin Joo Ryu

    (Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu)

  • Seung-Jong Yoo

    (Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu
    Hanyang University)

  • Choongseok Song

    (Hanyang University)

  • Min Kyu Yang

    (Sahmyook University)

  • Doo Seok Jeong

    (Hanyang University)

  • Gun Hwan Kim

    (Korea Research Institute of Chemical Technology (KRICT) 141 Gajeong-Ro, Yuseong-Gu)

Abstract

Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency ( 104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 μs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

Suggested Citation

  • Kanghyeok Jeon & Jeeson Kim & Jin Joo Ryu & Seung-Jong Yoo & Choongseok Song & Min Kyu Yang & Doo Seok Jeong & Gun Hwan Kim, 2021. "Self-rectifying resistive memory in passive crossbar arrays," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23180-2
    DOI: 10.1038/s41467-021-23180-2
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    Cited by:

    1. Choi, Woo Sik & Kim, Donguk & Yang, Tae Jun & Chae, Inseok & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Electrode-dependent electrical switching characteristics of InGaZnO memristor," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Sanghyeon Choi & Jaeho Shin & Gwanyeong Park & Jung Sun Eo & Jingon Jang & J. Joshua Yang & Gunuk Wang, 2024. "3D-integrated multilayered physical reservoir array for learning and forecasting time-series information," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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