Unidirectional information flow in a nanomagnetic metamaterial

2026-04-10

AI Summary

Conventional artificial spin ice lacks intrinsic directionality, hindering directional information transport and limiting its utility in low-power computing. This work introduces a nonreciprocal interaction framework by engineering arrays of nanomagnets with geometrically encoded intrinsic directionality, enabling unidirectional domain wall propagation and reconfigurable magnetization reversal under a tunable external magnetic field. For the first time, this approach realizes an artificial spin ice system that supports unidirectional information flow. By integrating memory and computation within a single neuromorphic magnetic substrate, the proposed system significantly enhances memory capacity and computational performance in reservoir computing tasks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• artificial spin ice
• unidirectional information flow
• nanomagnetic metamaterial
• non-reciprocal transport
• domain propagation

Innovation

• artificial spin ice
• non-reciprocal transport
• unidirectional domain propagation
• reconfigurable directionality
• reservoir computing