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Updated: Jun 22, 2025

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Attojoule Hexagonal Boron Nitride-Based Memristor for High-Performance Neuromorphic Computing.

Jiye Kim1, Jaesub Song1, Hyunjoung Kwak1

  • 1Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, 37673, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

Hexagonal boron nitride memristors achieve attojoule energy levels for artificial neural networks. These devices offer high reliability and fast switching, mimicking biological synapses for next-generation computing.

Keywords:
attojoule energy consumptionhexagonal boron nitridememristormetal–organic chemical vapor depositionneuromorphic application

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computer Engineering

Background:

  • Next-generation neuromorphic computing demands energy-efficient and reliable memristors.
  • Current memristors struggle to match the low energy consumption of biological synapses.

Purpose of the Study:

  • To develop attojoule-level energy consumption memristors using hexagonal boron nitride (h-BN) for artificial neural networks.
  • To enhance the reliability and performance of memristive devices for neuromorphic applications.

Main Methods:

  • Fabrication of hexagonal boron nitride (h-BN)-based metal-insulator-semiconductor (MIS) memristors.
  • Wafer-scale uniform h-BN growth via metal-organic chemical vapor deposition (MOCVD) on highly doped Si.
  • Electrical and nano-structural analysis to elucidate switching mechanisms.

Main Results:

  • Demonstrated attojoule-level energy consumption in h-BN-based MIS memristors.
  • Achieved outstanding reliability and low variability due to uniform h-BN layer.
  • Observed nanosecond-level switching speeds and multi-resistance states enabled by atomic-scale filaments and SiOx layer.

Conclusions:

  • h-BN-based MIS memristors offer a pathway to overcome energy limitations in neuromorphic devices.
  • These memristors effectively bridge the gap between artificial and biological synapses.
  • The developed technology is well-suited for high-performance artificial neural networks.