Unidirectional information flow in a nanomagnetic metamaterial
This work addresses the challenge of low-power computing in nanomagnetic metamaterials by enabling unidirectional information flow, offering a neuromorphic platform that integrates memory and computation, though it appears incremental as it builds on existing ASI concepts with a new directional twist.
The researchers tackled the problem of limited information transmission in artificial spin ice (ASI) by developing a framework for non-reciprocal influence, discovering ASI geometries with inherent directionality that enable unidirectional domain movement, and experimentally demonstrating reconfigurable growth and improved memory in reservoir computing.
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.