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Learning through ferroelectric domain dynamics in solid-state synapses

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  • Sören Boyn

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
    Present address: Electrochemical Materials, ETH Zurich, 8092 Zurich, Switzerland)

  • Julie Grollier

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Gwendal Lecerf

    (University of Bordeaux, IMS, UMR 5218)

  • Bin Xu

    (University of Arkansas Fayetteville)

  • Nicolas Locatelli

    (Centre de Nanosciences et de Nanotechnologies, CNRS, Univ. Paris Sud, Université Paris-Saclay)

  • Stéphane Fusil

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Stéphanie Girod

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
    Present address: Materials Research and Technology Department, Luxembourg Institute of Science and Technology (LIST), 41 rue du Brill, L-4422 Belvaux, Luxembourg)

  • Cécile Carrétéro

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Karin Garcia

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Stéphane Xavier

    (Thales Research and Technology)

  • Jean Tomas

    (University of Bordeaux, IMS, UMR 5218)

  • Laurent Bellaiche

    (University of Arkansas Fayetteville)

  • Manuel Bibes

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Agnès Barthélémy

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

  • Sylvain Saïghi

    (University of Bordeaux, IMS, UMR 5218)

  • Vincent Garcia

    (Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay)

Abstract

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Suggested Citation

  • Sören Boyn & Julie Grollier & Gwendal Lecerf & Bin Xu & Nicolas Locatelli & Stéphane Fusil & Stéphanie Girod & Cécile Carrétéro & Karin Garcia & Stéphane Xavier & Jean Tomas & Laurent Bellaiche & Manu, 2017. "Learning through ferroelectric domain dynamics in solid-state synapses," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14736
    DOI: 10.1038/ncomms14736
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    Cited by:

    1. Rengjian Yu & Lihua He & Changsong Gao & Xianghong Zhang & Enlong Li & Tailiang Guo & Wenwu Li & Huipeng Chen, 2022. "Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Guangdi Feng & Qiuxiang Zhu & Xuefeng Liu & Luqiu Chen & Xiaoming Zhao & Jianquan Liu & Shaobing Xiong & Kexiang Shan & Zhenzhong Yang & Qinye Bao & Fangyu Yue & Hui Peng & Rong Huang & Xiaodong Tang , 2024. "A ferroelectric fin diode for robust non-volatile memory," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Tao Li & Yongyi Wu & Guoliang Yu & Shengxian Li & Yifeng Ren & Yadong Liu & Jiarui Liu & Hao Feng & Yu Deng & Mingxing Chen & Zhenyu Zhang & Tai Min, 2024. "Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP2S6," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    4. Kim, Tae-Hyeon & Kim, Sungjoon & Hong, Kyungho & Park, Jinwoo & Hwang, Yeongjin & Park, Byung-Gook & Kim, Hyungjin, 2021. "Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    5. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Zhen Luo & Zijian Wang & Zeyu Guan & Chao Ma & Letian Zhao & Chuanchuan Liu & Haoyang Sun & He Wang & Yue Lin & Xi Jin & Yuewei Yin & Xiaoguang Li, 2022. "High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Mitsumasa Nakajima & Katsuma Inoue & Kenji Tanaka & Yasuo Kuniyoshi & Toshikazu Hashimoto & Kohei Nakajima, 2022. "Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Fei Xue & Xin He & Yinchang Ma & Dongxing Zheng & Chenhui Zhang & Lain-Jong Li & Jr-Hau He & Bin Yu & Xixiang Zhang, 2021. "Unraveling the origin of ferroelectric resistance switching through the interfacial engineering of layered ferroelectric-metal junctions," Nature Communications, Nature, vol. 12(1), pages 1-8, December.

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