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<p>The rapid development of artificial intelligence (AI) and big data-driven edge intelligence applications has created an urgent demand for highly efficient computing hardware. Ferroelectric memristors have emerged as promising candidates for edge hardware due to their multi-level conductance tunability and high integration potential. In this work, we fabricated yttrium-doped hafnium oxide (YHO) memristors with a remanent polarization of ∼30 µC/cm<sup>2</sup>, a multi-level resistive state retention time of approximately 10<sup>5</sup> s, and an endurance of up to 10<sup>9</sup> cycles. Based on this device, we constructed a real-time path-tracking system for intelligent vehicles—which achieves 100% path recognition accuracy—and a traffic sign denoising network optimized for hardware mapping via a hierarchical mixed-precision quantization strategy; this network yields denoised images with a peak signal-to-noise ratio (PSNR) of 27.04 and a structural similarity index measure (SSIM) of 0.80. This work paves an innovative pathway for the practical application of hafnium-based ferroelectric memristors, accelerating the development of highly efficient hardware for edge intelligence.</p>

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Ultra-robust Y-doped hafnium oxide ferroelectric memristors for intelligent edge computing

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Biao Yang,
  • Weifeng Zhang,
  • Pengfei Li,
  • Yongqing Jia,
  • Xiaobing Yan

摘要

The rapid development of artificial intelligence (AI) and big data-driven edge intelligence applications has created an urgent demand for highly efficient computing hardware. Ferroelectric memristors have emerged as promising candidates for edge hardware due to their multi-level conductance tunability and high integration potential. In this work, we fabricated yttrium-doped hafnium oxide (YHO) memristors with a remanent polarization of ∼30 µC/cm2, a multi-level resistive state retention time of approximately 105 s, and an endurance of up to 109 cycles. Based on this device, we constructed a real-time path-tracking system for intelligent vehicles—which achieves 100% path recognition accuracy—and a traffic sign denoising network optimized for hardware mapping via a hierarchical mixed-precision quantization strategy; this network yields denoised images with a peak signal-to-noise ratio (PSNR) of 27.04 and a structural similarity index measure (SSIM) of 0.80. This work paves an innovative pathway for the practical application of hafnium-based ferroelectric memristors, accelerating the development of highly efficient hardware for edge intelligence.