Scholarlink

<p>Neuromorphic hardware requires memristive devices that combine stable resistive switching with controllable synaptic plasticity for reliable information processing. In this study, we report vanadium-doped β-Ga<sub>2</sub>O<sub>3</sub> memristors that achieve soft-forming resistive switching with enhanced endurance, retention, and tunable synaptic functions. Vanadium, introduced as an n-type dopant, creates controlled deep-level states and tailors the defect landscape, as confirmed by combined experimental measurements and density functional theory (DFT) calculations. This defect engineering stabilizes conductive filaments and modifies charge transport pathways, enabling robust defect-assisted conduction. The devices achieve short-term to long-term memory transitions (1-100 ms) via electrical pulse-width modulation, allowing precise control between binary synaptic states (“0” and “1”). Integrated into a 4-bit reservoir computing framework, the memristors generate distinct current-level distributions for binary sequences from 0000 to 1111, demonstrating strong nonlinear transformation for pattern recognition. These results position V-doped β-Ga<sub>2</sub>O<sub>3</sub> as a scalable, energy-efficient platform for next-generation neuromorphic computing and Internet of Things (IoT) applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Resistive switching and synaptic functionality in vanadium-doped gallium oxide-based memristors for neuromorphic and multi-bit reservoir computing

  • 期刊论文

  • 发表日期：

    2026年5月18日

查看全文
  • Ashish Kumar,
  • Shahid Iqbal,
  • Junmo Kim,
  • Hyumin Dang,
  • Hyungtak Seo

摘要

Neuromorphic hardware requires memristive devices that combine stable resistive switching with controllable synaptic plasticity for reliable information processing. In this study, we report vanadium-doped β-Ga2O3 memristors that achieve soft-forming resistive switching with enhanced endurance, retention, and tunable synaptic functions. Vanadium, introduced as an n-type dopant, creates controlled deep-level states and tailors the defect landscape, as confirmed by combined experimental measurements and density functional theory (DFT) calculations. This defect engineering stabilizes conductive filaments and modifies charge transport pathways, enabling robust defect-assisted conduction. The devices achieve short-term to long-term memory transitions (1-100 ms) via electrical pulse-width modulation, allowing precise control between binary synaptic states (“0” and “1”). Integrated into a 4-bit reservoir computing framework, the memristors generate distinct current-level distributions for binary sequences from 0000 to 1111, demonstrating strong nonlinear transformation for pattern recognition. These results position V-doped β-Ga2O3 as a scalable, energy-efficient platform for next-generation neuromorphic computing and Internet of Things (IoT) applications.