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A crossbar array of magnetoresistive memory devices for in-memory computing

Author

Listed:
  • Seungchul Jung

    (Samsung Electronics)

  • Hyungwoo Lee

    (Samsung Electronics)

  • Sungmeen Myung

    (Samsung Electronics)

  • Hyunsoo Kim

    (Samsung Electronics)

  • Seung Keun Yoon

    (Samsung Electronics)

  • Soon-Wan Kwon

    (Samsung Electronics)

  • Yongmin Ju

    (Samsung Electronics)

  • Minje Kim

    (Samsung Electronics)

  • Wooseok Yi

    (Samsung Electronics)

  • Shinhee Han

    (Samsung Electronics)

  • Baeseong Kwon

    (Samsung Electronics)

  • Boyoung Seo

    (Samsung Electronics)

  • Kilho Lee

    (Samsung Electronics)

  • Gwan-Hyeob Koh

    (Samsung Electronics)

  • Kangho Lee

    (Samsung Electronics)

  • Yoonjong Song

    (Samsung Electronics)

  • Changkyu Choi

    (Samsung Electronics)

  • Donhee Ham

    (Samsung Electronics
    Harvard University)

  • Sang Joon Kim

    (Samsung Electronics)

Abstract

Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches1–3. One notable example is in-memory computing based on crossbar arrays of non-volatile memories4–7 that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks. Various non-volatile memories—including resistive memory8–13, phase-change memory14,15 and flash memory16–19—have been used for such approaches. However, it remains challenging to develop a crossbar array of spin-transfer-torque magnetoresistive random-access memory (MRAM)20–22, despite the technology’s practical advantages such as endurance and large-scale commercialization5. The difficulty stems from the low resistance of MRAM, which would result in large power consumption in a conventional crossbar array that uses current summation for analogue multiply–accumulate operations. Here we report a 64 × 64 crossbar array based on MRAM cells that overcomes the low-resistance issue with an architecture that uses resistance summation for analogue multiply–accumulate operations. The array is integrated with readout electronics in 28-nanometre complementary metal–oxide–semiconductor technology. Using this array, a two-layer perceptron is implemented to classify 10,000 Modified National Institute of Standards and Technology digits with an accuracy of 93.23 per cent (software baseline: 95.24 per cent). In an emulation of a deeper, eight-layer Visual Geometry Group-8 neural network with measured errors, the classification accuracy improves to 98.86 per cent (software baseline: 99.28 per cent). We also use the array to implement a single layer in a ten-layer neural network to realize face detection with an accuracy of 93.4 per cent.

Suggested Citation

  • Seungchul Jung & Hyungwoo Lee & Sungmeen Myung & Hyunsoo Kim & Seung Keun Yoon & Soon-Wan Kwon & Yongmin Ju & Minje Kim & Wooseok Yi & Shinhee Han & Baeseong Kwon & Boyoung Seo & Kilho Lee & Gwan-Hyeo, 2022. "A crossbar array of magnetoresistive memory devices for in-memory computing," Nature, Nature, vol. 601(7892), pages 211-216, January.
  • Handle: RePEc:nat:nature:v:601:y:2022:i:7892:d:10.1038_s41586-021-04196-6
    DOI: 10.1038/s41586-021-04196-6
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    Cited by:

    1. Yahong Chai & Yuhan Liang & Cancheng Xiao & Yue Wang & Bo Li & Dingsong Jiang & Pratap Pal & Yongjian Tang & Hetian Chen & Yuejie Zhang & Hao Bai & Teng Xu & Wanjun Jiang & Witold Skowroński & Qinghua, 2024. "Voltage control of multiferroic magnon torque for reconfigurable logic-in-memory," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Qu Yang & Donghyeon Han & Shishun Zhao & Jaimin Kang & Fei Wang & Sung-Chul Lee & Jiayu Lei & Kyung-Jin Lee & Byong-Guk Park & Hyunsoo Yang, 2024. "Field-free spin–orbit torque switching in ferromagnetic trilayers at sub-ns timescales," Nature Communications, Nature, vol. 15(1), pages 1-6, December.
    3. Fadi Jebali & Atreya Majumdar & Clément Turck & Kamel-Eddine Harabi & Mathieu-Coumba Faye & Eloi Muhr & Jean-Pierre Walder & Oleksandr Bilousov & Amadéo Michaud & Elisa Vianello & Tifenn Hirtzlin & Fr, 2024. "Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Chao Yun & Zhongyu Liang & Aleš Hrabec & Zhentao Liu & Mantao Huang & Leran Wang & Yifei Xiao & Yikun Fang & Wei Li & Wenyun Yang & Yanglong Hou & Jinbo Yang & Laura J. Heyderman & Pietro Gambardella , 2023. "Electrically programmable magnetic coupling in an Ising network exploiting solid-state ionic gating," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Sangyong Park & Dongyoung Lee & Juncheol Kang & Hojin Choi & Jin-Hong Park, 2023. "Laterally gated ferroelectric field effect transistor (LG-FeFET) using α-In2Se3 for stacked in-memory computing array," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Tie Mei & Chang Qing Chen, 2023. "In-memory mechanical computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Klaus Raab & Maarten A. Brems & Grischa Beneke & Takaaki Dohi & Jan Rothörl & Fabian Kammerbauer & Johan H. Mentink & Mathias Kläui, 2022. "Brownian reservoir computing realized using geometrically confined skyrmion dynamics," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    9. Xi Zhou & Liang Zhao & Chu Yan & Weili Zhen & Yinyue Lin & Le Li & Guanlin Du & Linfeng Lu & Shan-Ting Zhang & Zhichao Lu & Dongdong Li, 2023. "Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    10. Long Liu & Di Wang & Dandan Wang & Yan Sun & Huai Lin & Xiliang Gong & Yifan Zhang & Ruifeng Tang & Zhihong Mai & Zhipeng Hou & Yumeng Yang & Peng Li & Lan Wang & Qing Luo & Ling Li & Guozhong Xing & , 2024. "Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    11. Yudi Dai & Junlin Xiong & Yanfeng Ge & Bin Cheng & Lizheng Wang & Pengfei Wang & Zenglin Liu & Shengnan Yan & Cuiwei Zhang & Xianghan Xu & Youguo Shi & Sang-Wook Cheong & Cong Xiao & Shengyuan A. Yang, 2024. "Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    12. Piyush Agarwal & Lisen Huang & Sze Lim & Ranjan Singh, 2022. "Electric-field control of nonlinear THz spintronic emitters," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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