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<p>The development of analog memristive devices with symmetric and controllable switching is critical for the implementation of neuromorphic hardware capable of spike-based supervised learning. Here, we report a TiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> bilayer memristor that exhibits filamentary-driven, gradual dual bipolar resistive switching, enabling both spike-timing-dependent plasticity (STDP) and anti-STDP within a single device. The resistance of the TiO<sub>2</sub> layer is modulated by the growth dynamics of oxygen vacancy filaments in the Al<sub>2</sub>O<sub>3</sub> layer, which in turn alters the electric field distribution and enables progressive filament formation and rupture. This mechanism allows for the quasi-symmetric, polarity-reversible switching without external circuitry or explicit mode-switching procedures. The device demonstrates a high functional yield of 95% and a low switching voltage variation (coefficient of variation &lt; 3%). Crossbar array integration confirms reliable bidirectional synaptic updates in response to identical input spikes. Furthermore, a simulation incorporating experimentally extracted switching parameters achieves 84.5% classification accuracy on the MNIST dataset using the remote supervised method. These results establish the TiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> bilayer memristor as a scalable, CMOS-compatible platform for implementing biologically inspired learning rules in next-generation neuromorphic systems.</p>

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Filamentary-based gradual dual resistive switching in the TiO2/Al2O3 bilayer for remote supervised method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Woohyeon Ryu,
  • Suman Hu,
  • Chansoo Yoon,
  • Sohwi Kim,
  • Jihoon Jeon,
  • Gwangtaek Oh,
  • Yeonjoo Jeong,
  • Bae Ho Park

摘要

The development of analog memristive devices with symmetric and controllable switching is critical for the implementation of neuromorphic hardware capable of spike-based supervised learning. Here, we report a TiO2/Al2O3 bilayer memristor that exhibits filamentary-driven, gradual dual bipolar resistive switching, enabling both spike-timing-dependent plasticity (STDP) and anti-STDP within a single device. The resistance of the TiO2 layer is modulated by the growth dynamics of oxygen vacancy filaments in the Al2O3 layer, which in turn alters the electric field distribution and enables progressive filament formation and rupture. This mechanism allows for the quasi-symmetric, polarity-reversible switching without external circuitry or explicit mode-switching procedures. The device demonstrates a high functional yield of 95% and a low switching voltage variation (coefficient of variation < 3%). Crossbar array integration confirms reliable bidirectional synaptic updates in response to identical input spikes. Furthermore, a simulation incorporating experimentally extracted switching parameters achieves 84.5% classification accuracy on the MNIST dataset using the remote supervised method. These results establish the TiO2/Al2O3 bilayer memristor as a scalable, CMOS-compatible platform for implementing biologically inspired learning rules in next-generation neuromorphic systems.