ArXiv TLDR

Unidirectional information flow in a nanomagnetic metamaterial

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2604.09420

Johannes H. Jensen, Ida Breivik, Arthur Penty, Anders Strømberg, Henrik Tidemann Kaarbø + 8 more

cond-mat.mes-hallcs.ET

TLDR

This paper introduces a new family of artificial spin ice (ASI) geometries enabling unidirectional information flow, enhancing magnetic computing.

Key contributions

  • Developed a framework for non-reciprocal influence between nanomagnets.
  • Discovered a family of artificial spin ice (ASI) geometries with inherent directionality.
  • Experimentally demonstrated reconfigurable unidirectional domain growth in a directional ASI.
  • Showed improved memory capabilities in reservoir computing using this directional system.

Why it matters

This work presents the first artificial spin ice (ASI) with inherent directionality, overcoming prior limitations in information transmission. It offers a novel magnetic computing platform that integrates memory and computation within a single neuromorphic substrate.

Original Abstract

Artificial spin ice (ASI) are metamaterials composed of interacting nanomagnets. Although ASI hold promise for low-power computing, the ability to transmit information through these two-dimensional systems has been limited. Inspired by non-reciprocal transport in nature, we develop a framework for non-reciprocal influence between nanomagnets. Using the framework we discover a family of ASI geometries with inherent directionality. Directional ASI have the property that, when driven by an external field protocol, domains grow and reverse in the same direction, illustrating an emergent non-reciprocity of the system. Combining growth and reversal results in unidirectional domain movement through the metamaterial. We focus on one member of the directional ASI family, and demonstrate unidirectional domain growth experimentally. Furthermore, we show that the direction of growth is reconfigurable by tuning the external field strengths. Finally, we demonstrate how the directionality of the system significantly improves memory capabilities in a reservoir computing framework. Our work is the first demonstration of an ASI with inherent directionality, offering a magnetic computing platform that combines memory and computation within a single neuromorphic substrate.

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