A voltage-controlled reconfigurable memristor with dual-mode synaptic plasticity for adaptive neuromorphic computing and mechanism analysis
Source abstract
Abstract
Abstract In this study, we propose and experimentally validate a simple planar-integrated dual-device memristor structure based on HfO 2 /Al 2 O 3 heterostructure, which demonstrates voltage-modulated switching behavior between analog and digital resistance switch (A-RS/D-RS) modes. Low-voltage operation (less than 1 V) achieves A-RS for continuous conductance modulation (emulating synaptic plasticity), while higher voltages (more than 2 V) trigger D-RS for abrupt threshold transitions (suitable for binary neural networks). 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. COMSOL multiphysics simulations further confirmed our elucidation of the switching mechanism. This functional versatility demonstrates critical cross-platform 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xidian University
Funding
Opening Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laborator · National Natural Science Foundation of China
References (33)
Show source-provided references
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