A voltage-controlled reconfigurable memristor with dual-mode synaptic plasticity for adaptive neuromorphic computing and mechanism analysis
Year: 2026
Type: article
Abstract: 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: Nanotechnology
Authors: Kai Sun, Hongxia Liu, Ye Yang, Zhenli Zuo
Institution: Xidian University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
Award: G4170341743
OpenAlex ID: W7161593886