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<p>Replicating brain-like computation with fluidic memristors offers advantages in energy efficiency and chemical responsiveness over solid-state devices, yet scaling remains challenging due to complex fabrication and their amorphous nature. Herein, we developed a confined hydrogel fluidic memristor by forming a gel-gel interface at the micropore orifice. This design with confined hydrogel enables scalable fabrication of a 10×10 fluidic memristor array (FMA) on polyimide micropores. FMA exhibits fundamental neuromorphic behaviors like paired-pulse facilitation/depression, spike-rate-dependent plasticity, and chemical-regulated plasticity. We also used reservoir computing algorithms with FMA to recognize both computer-generated black-and-white digit images and handwritten digits, achieving a classification accuracy of 89.5% on the Modified National Institute of Standards and Technology dataset. This study demonstrates a hydrogel confined fluidic memristor array, paving an avenue for creating large-scale fluidic memristor arrays and hardware intelligence with ions.</p>

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Confined-hydrogel fluidic memristor crossbar array for neuromorphic computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Guangguo Guo,
  • Tianyi Xiong,
  • Boyang Xie,
  • Jianping Zhang,
  • Jin Zhang,
  • Yinghai Lu,
  • Yueru Zhao,
  • Wenjie Ma,
  • Cong Pan,
  • Yanan Jiang,
  • Lanqun Mao,
  • Jianhua Wang,
  • Ping Yu

摘要

Replicating brain-like computation with fluidic memristors offers advantages in energy efficiency and chemical responsiveness over solid-state devices, yet scaling remains challenging due to complex fabrication and their amorphous nature. Herein, we developed a confined hydrogel fluidic memristor by forming a gel-gel interface at the micropore orifice. This design with confined hydrogel enables scalable fabrication of a 10×10 fluidic memristor array (FMA) on polyimide micropores. FMA exhibits fundamental neuromorphic behaviors like paired-pulse facilitation/depression, spike-rate-dependent plasticity, and chemical-regulated plasticity. We also used reservoir computing algorithms with FMA to recognize both computer-generated black-and-white digit images and handwritten digits, achieving a classification accuracy of 89.5% on the Modified National Institute of Standards and Technology dataset. This study demonstrates a hydrogel confined fluidic memristor array, paving an avenue for creating large-scale fluidic memristor arrays and hardware intelligence with ions.