Magnonic combinatorial memory
npj Spintronics volume 2, Article number: 2 (2024 ) Cite this article
In this work, we consider a type of magnetic memory where information is encoded into the mutual arrangement of magnets. The device is an active ring circuit comprising magnetic and electric parts connected in series. The electric part includes a broadband amplifier, phase shifters, and attenuators. The magnetic part is a mesh of magnonic waveguides with magnets placed on the waveguide junctions. There are amplitude and phase conditions for auto-oscillations to occur in the active ring circuit. The frequency(s) of the auto-oscillation and spin wave propagation path(s) in the magnetic part depends on the mutual arrangement of magnets in the mesh. The propagation path is detected with a set of power sensors. The correlation between circuit parameters and spin wave path is the basis of memory operation. The combination of input/output switches connecting electric and magnetic parts and electric phase shifters constitute the memory address. The output of the power sensors is the memory state. We present experimental data on the proof-of-the-concept experiments on the prototype with three magnets placed on top of a single-crystal yttrium iron garnet Y3Fe2(FeO4)3 (YIG) film. There are three selected places for the magnets to be placed. There is a variety of spin wave propagation paths for each configuration of magnets. The results demonstrate a robust operation with an On/Off ratio for path detection exceeding 35 dB at room temperature. The number of possible magnet arrangements scales factorially with the size of the magnetic part. The number of possible paths per one configuration scales factorial as well. It makes it possible to drastically increase the data storage density compared to conventional memory devices. Magnonic combinatorial memory with an array of 100 × 100 magnets can store all information generated by humankind. Physical limits and constraints are also discussed.
Information and communication technologies generate vast amounts of data that will far eclipse today’s data flows1. The global data will grow to 175 zettabytes (ZB) by 2025, according to the International Data Corporation2. Conventional storage systems may become unsustainable due to their limited data capacity, infrastructure cost, and power consumption3. For example, flash-memory manufacturers would need ∼ 109 kg of silicon wafers even though the total projected wafer supply is ∼ 107–108 kg1. There is an urgent need for increasing the data storage density (i.e., the number of bits stored per area). In the traditional process of improving the data storage density, better performance is achieved by the miniaturization of the data-storage elements. It stimulates a quest for nanometer-size memory elements such as DNA-based4 or sequence-defined macromolecules5. At the same time, memory architecture and the principles of data storage remain mainly unchanged for the last 50 years.
