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Home
Programmable Ferroelectric Rectifier For Reliable And Efficient Neuromorphic Crossbar Array.

Home
Programmable Ferroelectric Rectifier For Reliable And Efficient Neuromorphic Crossbar Array.

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Programmable ferroelectric rectifier for reliable and efficient neuromorphic crossbar array.

Youngmin Kim¹, Yoon Jung Lee^1,2,3, Jiwoong Yang⁴

¹Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, Republic of Korea.

Nature Communications

|March 18, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed novel memristor devices using barium-substituted bismuth ferrite (BBFO) for energy-efficient neuromorphic computing. These devices exhibit excellent rectification and synaptic behavior, paving the way for advanced computing hardware.

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Area of Science:

Materials Science
Solid State Physics
Computer Engineering

Background:

Neuromorphic computing hardware demands energy efficiency, scalability, and reliable conduction.
Ferroelectric materials in memristors offer a promising solution through electrostatically modulated conduction.

Purpose of the Study:

To develop and characterize memristor devices using functional ferroelectric materials for neuromorphic applications.
To investigate the role of material composition in achieving high-performance memristive behavior.

Main Methods:

Fabrication of epitaxial heterostructures: Pt/Ba₀.₂Bi₀.₈FeO₃ (BBFO)/SrRuO₃/SrTiO₃.
Characterization of memristor devices for electrical properties, including rectifying ratio and off-state current.
Analysis of ferroelectric polarization and oxygen vacancy migration under pulsed bias.

Main Results:

Memristor devices achieved a rectifying ratio > 10⁶ and off-state current < 10⁻¹² A.
20% Ba substitution in BiFeO₃ coupled ferroelectric polarization with oxygen vacancy migration, enabling reliable synaptic behavior (>10⁷ cycles, near-zero nonlinearity).
Selector-free crossbar arrays (CBAs) were implemented, mitigating sneak currents and cell variability.

Conclusions:

BBFO-based memristors offer a robust material platform for high-performance neuromorphic systems.
The developed devices address key challenges in energy-efficient and scalable neuromorphic hardware.
BBFO's unique properties enable selector-free CBA implementation for advanced computing.