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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Interface engineered V₂O₅-based flexible memristors towards high-performance brain-inspired neuromorphic computing.

Kumar Kaushlendra¹, Davinder Kaur¹

¹Functional Nanomaterials Research Laboratory (FNRL), Department of Physics and Centre for Nanotechnology, Indian Institute of Technology Roorkee, Uttarakhand, India. davinder.kaur@ph.iitr.ac.in.

Nanoscale Horizons

|December 17, 2025

Summary

This study introduces flexible V2O5-based resistive memory devices for neuromorphic computing. Device D2 shows superior gradual resistive switching and synaptic behavior, achieving high accuracy in neural network simulations.

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Area of Science:

Materials Science
Electronics Engineering
Computer Science

Background:

Neuromorphic computing aims to mimic brain functions in electronic systems.
Flexible electronics offer new possibilities for advanced computing architectures.
Resistive switching memory devices are key components for neuromorphic applications.

Purpose of the Study:

To develop and characterize flexible resistive memory devices based on V2O5.
To investigate the impact of interfacial layers on device performance for synaptic applications.
To evaluate the potential of these devices for artificial neural networks.

Main Methods:

Fabrication of two flexible resistive memory devices (D1 and D2) using DC magnetron sputtering.
Characterization of resistive switching behavior, including SET/RESET transitions and multilevel states.
XPS depth profiling to analyze interfacial layers and understand switching mechanisms.
Testing of synaptic plasticity (LTP/LTD) and neural dynamics emulation.
Simulation of an artificial neural network using device characteristics.

Main Results:

Device D2 demonstrated fully gradual resistive switching (GRS) with enhanced synaptic fidelity due to NbOx/NiO interfacial layers.
Both devices exhibited multilevel resistive states and replicated biological synaptic functions.
Device D2 showed a higher memory window (~552) and endurance (>7000 cycles) compared to D1.
Simulated neural network using D2 achieved ~86.75% accuracy, attributed to linear analog weight modulation.

Conclusions:

V2O5-based flexible resistive memory devices show significant promise for high-performance neuromorphic computing.
The synergistic effect of interfacial layers in D2 enhances analog synaptic behavior and device reliability.
Device D2's characteristics are highly suitable for advanced artificial neural network applications.