Engineering Relaxation Pathways in Building Blocks of Artificial Spin Ice for Computation

TL;DR

This paper explores how engineered arrangements of thermally-active nanomagnets in artificial spin ice can perform both deterministic and probabilistic computation by controlling relaxation pathways and intermagnet distances.

Contribution

It introduces a method to engineer thermal relaxation pathways in artificial spin ice for computation and demonstrates tuning of output probabilities and interconnected computational blocks.

Findings

Engineered relaxation pathways enable deterministic and probabilistic computation.

Adjusting intermagnet distance tunes the probability of specific outcomes.

Connected building blocks demonstrate potential for complex computation.

Abstract

Nanomagnetic logic, which makes use of arrays of dipolar-coupled single domain nanomagnets for computation, holds promise as a low power alternative to traditional computation with CMOS. Beyond the use of nanomagnets for Boolean logic, nanomagnets can also be exploited for non-deterministic computational schemes such as edge detection in images and for solving the traveling salesman problem. Here, we demonstrate the potential of arrangements of thermally-active nanomagnets based on artificial spin ice for both deterministic and probabilistic computation. This is achieved by engineering structures that follow particular thermal relaxation pathway consisting of a sequence of reorientations of magnet moments from an initial field-set state to a final low energy output state. Additionally, we demonstrate that it is possible to tune the probability of attaining a particular final low-energy state, and therefore the likelihood of a given output, by modifying the intermagnet distance. Finally, we experimentally demonstrate a scheme to connect several computational building blocks for complex computation.