Printing circuits on rare nanomagnets puts a new spin on computing

New research could lead to a new paradigm in artificial intelligence by allowing the creation of physical hardware. A form of artificial intelligence that can recognize objects from partial images is possible because of the unusual properties of spin glass.

The first experimental realization of an artificial spin glass consisting of nanomagnets arranged to replicate a neural network was accomplished by Michael Saccone, a post-doctoral researcher in theoretical physics at Los Alamos National Laboratory.

Spin glasses can be used to think about material structure. Being free, for the first time, to modify the interaction within these systems using electron-beam lithography makes it possible to represent a variety of computing problems in spin-glass networks.

Spin-glass systems arise from random interactions and competition between two types of magnetic order in the material and are engineered at the intersection of computation and engineered materials. They exhibitfrustration, which means that they don't settle into a uniformly ordered configuration when their temperature drops, and they have distinct thermodynamic and dynamic qualities that can be harnessed for computing applications.

Theoretical models describing spin glasses are used in other complex systems, such as those describing brain function, error-correcting codes or stock-market dynamics.

The research team combined theoretical and experimental work to fabricate and observe the artificial spin glass as a proof-of-principle Hopfield neural network.

One field feeds off the other in the symbiotic relationship between spin glass and Hopfield networks. Neural networks can link two or more memory patterns related to an object. The network can recall the complete face if just one memory is triggered. Associative memory doesn't need a perfectly identical scenario to identify a memory.

The memories of these networks correspond to the ground states of a spin system and are not disturbed by noise.

The material was a spin glass, evidence that will allow the team to describe the properties of the system and how it processes information. Artificial intelligence would be moremessier than traditional programs, but also more flexible for some applications, according to Saccone.

More information: Michael Saccone et al, Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network, Nature Physics (2022). DOI: 10.1038/s41567-022-01538-7 Journal information: Nature Physics Citation: Printing circuits on rare nanomagnets puts a new spin on computing (2022, March 28) retrieved 28 March 2022 from https://phys.org/news/2022-03-circuits-rare-nanomagnets.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.