Magnonics is an engineering subfield that strives to develop information technology in terms of speed, device architecture, and energy usage, similar to electronics or photonics. A magnon is the precise amount of energy needed to cause a collective excitation known as a spin wave to change a material’s magnetization.
Magnons can be used to encode and transfer data without using electron fluxes, which waste energy by heating (known as Joule heating) the conductor being used. Magnons interact with magnetic fields.
As Dirk Grundler, head of the Lab of Nanoscale Magnetic Materials and Magnonics (LMGN) in the School of Engineering explains, energy losses are an increasingly serious barrier to electronics as data speeds and storage demands soar.
“With the advent of AI, the use of computing technology has increased so much that energy consumption threatens its development,” Grundler says. “A major issue is traditional computing architecture, which separates processors and memory. The signal conversions involved in moving data between different components slow down computation and waste energy.”
Researchers are looking for new computing architectures that can more effectively handle the demands of large data in order to address this inefficiency, also known as the memory wall or Von Neumann bottleneck. And now, Grundler believes his lab might have stumbled on such a “holy grail.”
With the advent of AI, the use of computing technology has increased so much that energy consumption threatens its development. A major issue is traditional computing architecture, which separates processors and memory. The signal conversions involved in moving data between different components slow down computation and waste energy.
Dirk Grundler
While doing other experiments on a commercial wafer of the ferrimagnetic insulator yttrium iron garnet (YIG) with nanomagnetic strips on its surface, LMGN PhD student Korbinian Baumgaertl was inspired to develop precisely engineered YIG-nanomagnet devices.
With the Center of MicroNanoTechnology’s support, Baumgaertl was able to excite spin waves in the YIG at specific gigahertz frequencies using radiofrequency signals, and crucially to reverse the magnetization of the surface nanomagnets.
“The two possible orientations of these nanomagnets represent magnetic states 0 and 1, which allows digital information to be encoded and stored,” Grundler explains.
A route to in-memory computation
The researchers used a traditional vector network analyzer to send a spin pulse through the YIG-nanomagnet device and make their discovery. Data could then be written to and read from using nanomagnet reversal, which only occurred when the spin wave reached a specific amplitude.
“We can now show that the same waves we use for data processing can be used to switch the magnetic nanostructures so that we also have nonvolatile magnetic storage within the very same system,” Grundler explains, adding that “nonvolatile” refers to the stable storage of data over long time periods without additional energy consumption.
The capability of the technology to combine data processing and storage eliminates the energy-inefficient separation of processors and memory storage and enables in-memory computation, which has the potential to alter the present computing architectural paradigm.
Optimization on the horizon
The ground-breaking findings were published by Baumgaertl and Grundler in the journal Nature Communications, and the LMGN team is now working to improve their methodology.
“Now that we have shown that spin waves write data by switching the nanomagnets from states 0 to 1, we need to work on a process to switch them back again this is known as toggle switching,” Grundler says.
He also notes that theoretically, “the magnonics approach could process data in the terahertz range of the electromagnetic spectrum (for comparison, current computers function in the slower gigahertz range). However, they still need to demonstrate this experimentally.”
“The promise of this technology for more sustainable computing is huge. With this publication, we are hoping to reinforce interest in wave-based computation, and attract more young researchers to the growing field of magnonics.”
