A major problem is traditional computer architecture, which separates processors and memory. The signal conversions involved in moving data between different components slow down computation and waste energy. This inefficiency is known as the memory wall or Von Neumann bottleneck.

There is a challenging search for new approaches to overcome this so-called Von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum allows energy-efficient calculations without load current.

Magnons are the quanta of spin waves. Because they interact with magnetic fields, magnons can be used to encode and transport data without electron flows, which involves energy loss from heating (known as Joule heating) of the conductor used.

Researchers at EPFL have successfully transmitted and stored data using charge-free magnetic waves instead of conventional electron currents, thanks to a breakthrough in magnonics. The problem of energy-consuming computing technologies in the big data era can now be solved thanks to the finding.

While doing other experiments on a commercial wafer of the ferrimagnetic insulator yttrium iron garnet (YIG) with nanomagnetic strips on the surface, LMGN Ph.D. student Korbinian Baumgaertl was inspired to develop precisely designed YIG nanomagnet devices. With support from the Center of MicroNanoTechnology, Baumgaertl was able to excite spin waves in the YIG at specific gigahertz frequencies using radio-frequency signals and—critically—reverse the magnetization of the surface nanomagnets.

Dirk Grundler, head of the Lab of Nanoscale Magnetic Materials and Magnonics (LMGN) at the School of Engineering, explains: “The two possible orientations of these nanomagnets represent magnetic states 0 and 1, allowing digital information to be encoded and stored.”

“Theoretically, the magnonic approach could process data in the terahertz range of the electromagnetic spectrum (for comparison, current computers operate in the slower gigahertz range). However, they still have to prove this experimentally.”

“The promise of this technology for more durable computers is huge. With this publication, we hope to strengthen the interest in wave-based computation and attract more young researchers to the growing field of magnonics.”

Magazine reference:

  1. Baumgaertl, K., Grundler, D. Reversal of nanomagnets by dispersing magnons in ferrimagnetic yttrium iron garnet enabling non-volatile magnon memory. Nat Commun 14, 1490 (2023). DOI: 10.1038/s41467-023-37078-8