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Ferromagnetism

Ferromagnetism

Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...

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A stationary charge creates and interacts with the electric field, while a moving charge creates a magnetic field.
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Related Experiment Video

Updated: Apr 16, 2026

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

Engineering and Exploiting Self-Driven Domain Wall Motion in Ferrimagnets for Neuromorphic Computing Applications.

Jeffrey A Brock^1,2, Aleksandr Kurenkov^1,2, David R Lindenmann^1,2

¹Laboratory for Mesoscopic Systems, Department of Materials, ETH Zurich, 8093 Zurich, Switzerland.

|April 14, 2026

Summary

This summary is machine-generated.

Engineered magnetic domain walls in ferrimagnets enable efficient neuromorphic computing. This research demonstrates control over domain wall motion for brain-inspired computing functionalities, offering a scalable platform.

Keywords:

ferrimagnets magnetic domain walls neuromorphic computing spintronics

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Last Updated: Apr 16, 2026

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Area of Science:

Materials Science
Condensed Matter Physics
Computer Engineering

Background:

Magnetic domain wall motion is a promising mechanism for energy-efficient, brain-inspired computing.
Implementing all necessary neuromorphic functionalities in standard materials is challenging due to competing interactions.

Purpose of the Study:

To demonstrate how engineered lateral exchange coupling in ferrimagnets can achieve neuromorphic computing functionalities.
To establish a tunable, scalable platform for domain wall-based computing.

Main Methods:

Utilized experiments and micromagnetic simulations.
Engineered lateral exchange coupling in transition metal-rare earth ferrimagnets.
Tuned feature size, material composition, and chiral interaction strength.

Main Results:

Achieved spontaneous domain wall motion for neuromorphic functionalities with minimal complexity.
Demonstrated control over domain wall motion speed.
Integrated with current-induced spin-orbit torques to enable leaky integration and passive resetting of artificial neurons.

Conclusions:

Locally engineered ferrimagnets provide a tunable and scalable platform for domain wall-based neuromorphic computing.
Spontaneous domain wall motion offers a straightforward approach for next-generation computing architectures.