Pattern recognition using spiking antiferromagnetic neurons.

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex.

Lumped circuit model for inductive antenna spin-wave transducers.

We derive a lumped circuit model for inductive antenna spin-wave transducers in the vicinity of a ferromagnetic medium. The model considers the antenna's Ohmic resistance, its inductance, as well as the additional inductance due to the excitation of ferromagnetic resonance or spin waves in the ferromagnetic medium. As an example, the additional inductance is discussed for a wire antenna on top of a ferromagnetic waveguide, a structure that is characteristic for many magnonic devices and experiments.

Ultrafast GHz-Range Swept-Tuned Spectrum Analyzer with 20 ns Temporal Resolution Based on a Spin-Torque Nano-Oscillator with a Uniformly Magnetized "Free" Layer.

The advantage of an ultrafast frequency-tunability of spin-torque nano-oscillators (STNOs) that have a large (>100 MHz) relaxation frequency of amplitude fluctuations is exploited to realize ultrafast wide-band time-resolved spectral analysis at nanosecond time scale with a frequency resolution limited only by the "bandwidth" theorem. The demonstration is performed with an STNO generating in the 9 GHz frequency range and comprised of a perpendicular polarizer and a perpendicularly and uniformly magnetized "free" layer. It is shown that such a uniform-state STNO-based spectrum analyzer can efficiently perform spectral analysis of frequency-agile signals with rapidly varying frequency components.

Controlling Magnon Interaction by a Nanoscale Switch.

The ability to control and tune magnetic dissipation is a key concept of emergent spintronic technologies. Magnon scattering processes constitute a major dissipation channel in nanomagnets, redefine their response to spin torque, and hold the promise for manipulating magnetic states on the quantum level. Controlling these processes in nanomagnets, while being imperative for spintronic applications, has remained difficult to achieve.

Ultrafast Sweep-Tuned Spectrum Analyzer with Temporal Resolution Based on a Spin-Torque Nano-Oscillator.

We demonstrate that a spin-torque nano-oscillator (STNO) rapidly sweep-tuned by a bias voltage can be used to perform an ultrafast time-resolved spectral analysis of frequency-manipulated microwave signals. The critical reduction in the time of the spectral analysis comes from the naturally small-time constants of a nanosized STNO (1-100 ns). The demonstration is performed on a vortex-state STNO generating in a frequency range around 300 MHz, when frequency down-conversion and matched filtering is used for signal processing.