Schematic of the proposed device structure for neuromorphic spintronic memristors. The write path is between the terminals through the top layer (black dotted line); the read path goes through the device stack (red dotted line). The right side of the image shows how the choice of substrate dictates whether the device will show deterministic or probabilistic behavior. Image: Banerjee group, University of Groningen.
Schematic of the proposed device structure for neuromorphic spintronic memristors. The write path is between the terminals through the top layer (black dotted line); the read path goes through the device stack (red dotted line). The right side of the image shows how the choice of substrate dictates whether the device will show deterministic or probabilistic behavior. Image: Banerjee group, University of Groningen.

Conventional computers use binary values (0 and 1) to perform their calculations. By contrast, our brain cells are able to use more values to operate, making them more energy-efficient than computers, which is why scientists are interested in neuromorphic (brain-like) computing.

Using a complex metal oxide that can take advantage of spins, a magnetic property of electrons, physicists from the University of Groningen in the Netherlands have now created computing elements comparable to the neurons and synapses in the brain. They report their work in a paper in Frontiers in Nanotechnology.

Although computers can do straightforward calculations much faster than humans, our brains outperform silicon machines in tasks like object recognition. Furthermore, our brains use much less energy than computers. In part, this can be explained by the way our brain operates: whereas a computer uses a binary system (with values 0 or 1), brain cells utilize analogue signals with a range of values.

Although the operation of our brains can be simulated in computers, the basic architecture still relies on a binary system. That is why scientists are looking at other approaches, such as creating hardware that is more brain-like but will still interface with normal computers.

"One idea is to create magnetic bits that can have intermediate states," says Tamalika Banerjee, professor of spintronics of functional materials at the University of Groningen's Zernike Institute for Advanced Materials. She works on spintronics, which uses a magnetic property of electrons called 'spin' to transport, manipulate and store information.

In this study, her PhD student Anouk Goossens, first author of the paper, synthesized thin films of a ferromagnetic metal called strontium-ruthenate oxide (SRO) on a substrate of strontium titanate oxide. This SRO thin film contained magnetic domains that were perpendicular to the plane of the film.

"These can be switched more efficiently than in-plane magnetic domains," explains Goossens. By varying the growth conditions, it proved possible to control the crystal orientation in the SRO. Previously, out-of-plane magnetic domains have been made using other techniques, but these typically require complex layered structures.

The magnetic domains can then be switched using a current applied through a platinum electrode on top of the SRO. "When the magnetic domains are oriented perfectly perpendicular to the film, this switching is deterministic: the entire domain will switch," says Goossens. However, when the magnetic domains are slightly tilted, the response is probabilistic: not all the domains are the same, and intermediate values occur when only part of the crystals in the domain have switched.

By choosing variants of the substrate on which the SRO film is grown, the scientists can control its magnetic anisotropy, allowing them to produce two different spintronic devices. "This magnetic anisotropy is exactly what we wanted," says Goossens. "Probabilistic switching compares to how neurons function, while the deterministic switching is more like a synapse."

The scientists expect that, in the future, brain-like computer hardware can be created by combining these different domains in a spintronic device that can be connected to standard silicon-based circuits. Furthermore, probabilistic switching would also allow for stochastic computing, a promising technology that represents continuous values by streams of random bits.

"We have found a way to control intermediate states, not just for memory but also for computing," says Banerjee.

This story is adapted from material from the University of Groningen, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.