This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Artificial intelligence is transforming how information is generated, processed and stored, but its rapid expansion is also driving unprecedented demand for computing power and electricity. Developing hardware that can process information more efficiently is therefore becoming one of the major technological challenges of the AI era.
Researchers from an international collaboration involving the University of Edinburgh have demonstrated a new approach to brain-inspired computing based on tiny magnetic structures known as skyrmions. The research, published in Advanced Materials, shows how collective transformations of these magnetic structures can be used to create reliable artificial synapses that operate at room temperature. The human brain is remarkably energy efficient because memory and information processing occur together through networks of neurons and synapses.
Conventional computers, by contrast, continuously transfer information between physically separated processing and memory units. Neuromorphic computing seeks to overcome this limitation by developing electronic devices whose behavior more closely resembles biological neural networks. Magnetic skyrmions are particularly attractive for this purpose.
They are nanoscale, vortex-like arrangements of magnetic moments that behave as stable information carriers. Their small dimensions, nonvolatile nature and ability to respond to external stimuli have made them promising building blocks for future memory and computing technologies. However, previous approaches to skyrmion-based artificial synapses have often relied on creating or destroying individual skyrmions.
Because these processes can be inherently probabilistic, obtaining a predictable and reproducible response remains challenging. The new study takes a fundamentally different approach. Using the two-dimensional van der Waals ferromagnet Fe₃GaTe₂, the researchers exploit a collective transformation of the magnetic state from a skyrmion lattice into stripe-like magnetic domains.
Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically. This transformation produces a linear and highly reproducible change in the material's anomalous Hall resistance, an electrical signal that can be used to represent the strength, or "weight," of an artificial synapse. By changing the duration of the applied electrical pulses, the researchers can tune this synaptic weight, creating multiple information states and enabling the multiply-accumulate operations that underpin modern neural networks.
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