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Experimental Realization of the 1D Random Field Ising Model.

N S Bingham¹, S Rooke², J Park²

¹Department of Applied Physics, Yale University, New Haven, Connecticut 06511, USA.

Physical Review Letters

|December 3, 2021

Summary

This summary is machine-generated.

We observed magnetic-field-induced avalanches in artificial spin ice nanomagnets. The collective behavior statistics align with a 1D random field Ising model, revealing microscopic array structures.

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Area of Science:

Condensed Matter Physics
Magnetism
Materials Science

Background:

Artificial spin ice arrays are engineered magnetic systems with unique properties.
Understanding the collective behavior of interacting nanomagnets is crucial for developing novel magnetic devices.
Magnetic-field-induced avalanches are complex phenomena observed in various magnetic systems.

Purpose of the Study:

To investigate magnetic-field-induced avalanches in a square artificial spin ice array.
To characterize the evolution of nanomagnet moments under an incrementally increasing magnetic field.
To compare experimental results with theoretical models for insights into microscopic structures.

Main Methods:

Fabrication of a square artificial spin ice array composed of interacting nanomagnets.
Application of an incrementally increasing magnetic field to induce avalanches.
Imaging of individual nanomagnet moments after each field application to track configuration changes.
Statistical analysis of the moment configuration evolution.

Main Results:

Observed magnetic-field-induced avalanches in the artificial spin ice array.
The statistics of the moment configuration evolution showed good agreement with the canonical one-dimensional random field Ising model.
Macroscopic measurements of collective behavior provided insights into the microscopic structure of the arrays.

Conclusions:

The study successfully measured and characterized magnetic-field-induced avalanches in artificial spin ice.
The findings demonstrate the applicability of the 1D random field Ising model to understand these systems.
This work highlights a method for extracting microscopic structural information from macroscopic collective behavior measurements, applicable to other avalanche systems.