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Effects of voltage schemes on the conductance modulation of artificial synaptic device based on 2D hBN memristor: Its applications for pattern classifications

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  • Jo, Yooyeon
  • Noh, Gichang
  • Park, Eunpyo
  • Lee, Dae Kyu
  • Jeong, YeonJoo
  • Wi, Heerak
  • Kwak, Joon Young

Abstract

Artificial synaptic devices utilizing nonvolatile memristors with 2D materials show potential for neuromorphic computing systems due to their intriguing electrical properties, simple structure, and capability to emulate biological synaptic behaviors. Here, we fabricated nonvolatile memristors based on 2D multilayer hBN film and demonstrated their analog memory performance along with biological synaptic characteristics, including paired-pulse facilitation and depression (PPF and PPD), and synaptic plasticity. We utilized different voltage scheme algorithms to analyze the impact of synaptic functions on image classification tasks across various pattern datasets through simulations. Our findings reveal that pattern recognition outcomes varied due to the modulation of synaptic plasticity by the applied voltage schemes. We also investigated the relationship between conductance update variations and classification accuracy to highlight the significance of optimized synaptic plasticity in improving image recognition capabilities in large-scale neural networks. Our experimental results contribute to the development of high-performance neuromorphic computing for diverse pattern classification tasks using artificial synaptic devices based on 2D hBN.

Suggested Citation

  • Jo, Yooyeon & Noh, Gichang & Park, Eunpyo & Lee, Dae Kyu & Jeong, YeonJoo & Wi, Heerak & Kwak, Joon Young, 2024. "Effects of voltage schemes on the conductance modulation of artificial synaptic device based on 2D hBN memristor: Its applications for pattern classifications," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:chsofr:v:187:y:2024:i:c:s0960077924009421
    DOI: 10.1016/j.chaos.2024.115390
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    References listed on IDEAS

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    1. Helin Yang & Kwok-Yan Lam & Liang Xiao & Zehui Xiong & Hao Hu & Dusit Niyato & H. Vincent Poor, 2022. "Lead federated neuromorphic learning for wireless edge artificial intelligence," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Kwon, Osung & Kim, Sungjun & Agudov, Nikolay & Krichigin, Alexey & Mikhaylov, Alexey & Grimaudo, Roberto & Valenti, Davide & Spagnolo, Bernardo, 2022. "Non-volatile memory characteristics of a Ti/HfO2/Pt synaptic device with a crossbar array structure," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. 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.
    4. Helin Yang & Kwok-Yan Lam & Liang Xiao & Zehui Xiong & Hao Hu & Dusit Niyato & H. Vincent Poor, 2022. "Author Correction: Lead federated neuromorphic learning for wireless edge artificial intelligence," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    5. Dmitry Kireev & Samuel Liu & Harrison Jin & T. Patrick Xiao & Christopher H. Bennett & Deji Akinwande & Jean Anne C. Incorvia, 2022. "Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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