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May 21, 2026Nanotechnology0 citationsOpen Access

A voltage-controlled reconfigurable memristor with dual-mode synaptic plasticity for adaptive neuromorphic computing and mechanism analysis

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KSKai SunHLHongxia LiuYYYe Yang

Key Points

  • The study aims to investigate a memristor capable of dual-mode synaptic plasticity for neuromorphic computing.
  • Fabrication of a planar-integrated HfO₂/Al₂O₃ bilayer memristor.
  • Surface and interface analyses performed using X-ray photoelectron spectroscopy and atomic force microscopy.
  • Assessment of device performance through experimental validation in simulated array configurations.
  • Low-voltage operation (<1 V) achieves continuous conductance tuning, while high-voltage pulses (>2 V) result in binary resistive behavior.
  • Demonstrated adaptability across neural network architectures, enhancing compatibility within neuromorphic systems.
  • Exhibited strong potential for energy-efficient reconfigurable computing implementations.

Abstract

ABSTRACT:In this study, we report a planar-integrated memristor fabricated with a CMOScompatible HfO₂/Al₂O₃ bilayer, in which voltage-controlled redistribution of oxygen vacancies at the HfO₂/Al₂O₃ interface enables reversible switching between analog and digital resistive states within a single device. Comprehensive surface and interface analyses-including X-ray photoelectron spectroscopy (XPS) profiling, atomic force microscopy (AFM), and temperaturedependent electrical transport-reveal that low-voltage operation (2 V) trigger abrupt filamentary conduction via field-driven aggregation of oxygen vacancies, resulting in binary resistive behavior. The dual-mode functionality is directly correlated with the electric-fieldmediated evolution of interfacial defect profiles and energy barriers, as supported by conduction mechanism modeling (e.g., Poole-Frenkel emission and space-charge-limited conduction).Experimental validation through simulated array configurations empirically validates the device's capacity for achieving robust compatibility with diverse neural network architectures through its multi-model operational capabilities. This functional versatility demonstrates critical crossplatform adaptability essential for neuromorphic computing implementations. This dynamically mode-switchable device between dual resistance switching modes offers a scalable solution for energy-efficient reconfigurable neuromorphic systems, demonstrating promising potential in next-generation intelligent sensing-computing co-architectures.

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Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a0ea0f7be05d6e3efb5f4f1https://doi.org/10.1088/1361-6528/ae6f23
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