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March 25, 2026Nano Research0 citationsOpen Access

Unipolar modulated volatile memristor: Achieving multi-scale plasticity, associative learning, and dynamic reservoir computing

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JLJie LuKWKexiang WangHLHaoyu Li

Key Points

  • The research aims to present a memristor that mimics brain functions, enhancing computing efficiency.
  • Developed a volatile memristor with an Ag/Al2O3/Ti/Al2O3/ITO structure.
  • Conducted experiments on conductivity modulation using unipolar pulses.
  • Investigated multi-scale plasticity effects like short-term and paired-pulse facilitation.
  • Evaluated device performance on image recognition tasks using the MNIST and Fashion-MNIST datasets.
  • Achieved accuracies of 97.05% for MNIST and 83.22% for Fashion-MNIST.
  • Observed normalized mean square errors of approximately 0.123 and 0.108 for two second-order nonlinear tasks.
  • Demonstrated functionality akin to classical conditioned reflexes in biological systems.

Abstract

The von Neumann architecture is nearing its physical limits regarding energy efficiency and parallelism. Consequently, brain-inspired computing hardware is viewed as a crucial solution to address these limitations. This research presents a volatile memristor featuring an Ag/Al2O3/Ti/Al2O3/ITO configuration for applications in brain-inspired computing. The device demonstrates conductivity modulation under unipolar pulses, attributed to the dynamic competition between electric field-driven drift and heat-induced diffusion, governed by the oxygen vacancy reservoir formed by the in situ oxidized Ti interlayer. The device exhibits multi-scale plasticity, encompassing short-term facilitation (STF), paired-pulse facilitation (PPF), and dependence on frequency and duty cycle. Switching between potentiation and depression can be achieved under the same polarity by tuning the strength of forward stimulation (amplitude, width, or interval). The device emulates the classical conditioned reflex of the spotted butterfly and establishes a dynamic reservoir, attaining accuracies of 97.05% and 83.22% in the MNIST and Fashion-MNIST image recognition tasks, with normalized mean square errors (NMSE) for the two second-order nonlinear system tasks of approximately 0.123 and 0.108, respectively. This study outlines a methodology for fabricating unipolar volatile memristors, thus enhancing the understanding of brain-inspired computing hardware.

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Cite This Study

Lu et al. (2026) studied this question.

synapsesocial.com/papers/69c37aa8b34aaaeb1a67c7f9https://doi.org/10.26599/nr.2026.94908641
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