The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate - precisely, rapidly and at low energy cost - the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.

Spin-torque building blocks

Magnetic nanostructures are being developed for use in many aspects of our daily life, spanning areas such as data storage, sensing and biomedicine. Whereas patterned nanomagnets are traditionally two-dimensional planar structures, recent work is expanding nanomagnetism into three dimensions; a move triggered by the advance of unconventional synthesis methods and the discovery of new magnetic effects. In three-dimensional nanomagnets more complex magnetic configurations become possible, many with unprecedented properties. Here we review the creation of these structures and their implications for the emergence of new physics, the development of instrumentation and computational methods, and exploitation in numerous applications.

/pdf/three-dimensional-nanomagnetism-2bkngvttqg.pdf

Three-dimensional nanomagnetism

Stripline (SL), vector network analyzer (VNA), and pulsed inductive microwave magnetometer (PIMM) techniques were used to measure the ferromagnetic resonance (FMR) linewidth for a series of Permalloy films with thicknesses of 50 and 100nm. The SL-FMR measurements were made for fixed frequencies from 1.5to5.5GHz. The VNA-FMR and PIMM measurements were made for fixed in-plane fields from 1.6to8kA∕m (20–100Oe). The results provide a confirmation, lacking until now, that the linewidths measured by these three methods are consistent and compatible. In the field format, the linewidths are a linear function of frequency, with a slope that corresponds to a nominal Landau-Lifshitz phenomenological damping parameter α value of 0.007 and zero frequency intercepts in the 160–320A∕m (2–4Oe) range. In the frequency format, the corresponding linewidth versus frequency response shows a weak upward curvature at the lowest measurement frequencies and a leveling off at high frequencies.

/pdf/ferromagnetic-resonance-linewidth-in-metallic-thin-films-4nmku8h46u.pdf

Ferromagnetic resonance linewidth in metallic thin films: Comparison of measurement methods | NIST

This paper reviews the state of the art in spin-torque and spin-Hall-effect-driven nano-oscillators. After a brief introduction to the underlying physics, the authors discuss different implementations of these oscillators, their functional properties in terms of frequency range, output power, phase noise, and modulation rates, and their inherent propensity for mutual synchronization. Finally, the potential for these oscillators in a wide range of applications, from microwave signal sources and detectors to neuromorphic computation elements, is discussed together with the specific electronic circuitry that has so far been designed to harness this potential.

Spin-Torque and Spin-Hall Nano-Oscillators

Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g., into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.

https://iopscience.iop.org/article/10.35848/1347-4065/ab8d4f/pdf

Physical reservoir computing—an introductory perspective

It is made possible to provide a spin-torque oscillator that has a high Q value and a high output. A spin-torque oscillator includes: an oscillating field generating unit configured to generate an oscillating field; and a magnetoresistive element including a magnetoresistive effect film including a first magnetization pinned layer of which a magnetization direction is pinned, a first magnetization free layer of which a magnetization direction oscillates with the oscillating field, and a first spacer layer interposed between the first magnetization pinned layer and the first magnetization free layer.

Spin-torque oscillator, magnetic head including the spin-torque oscillator, and magnetic recording and reproducing apparatus

For the application of the spin-torque oscillator to a high-data-transfer-rate read head, it is indispensable that the oscillation frequency responds promptly to the magnetic field from recorded bits. In this paper, we numerically exemplify the phase response to a short magnetic pulse. The phase basically follows the magnetic pulse although it takes several nanoseconds to return to the steady state because of the frequency nonlinearity. We also demonstrate the differential detection of recorded bits at the data-transfer rate beyond 5 Gbit/s.

Numerical Simulation on Temporal Response of Spin-Torque Oscillator to Magnetic Pulses

A read head based on a spin-torque oscillator (STO) has been proposed for the high-density magnetic recording beyond 2 Tbit/in.2. To evaluate the stability and agility of the oscillation, we conduct a real-time measurement of the STO waveform under a magnetic pulse (~60 Oe) in the nanosecond region, and estimate its instantaneous frequency. Stable oscillation is sustained in the presence of the magnetic pulse, and the frequency shift is clearly observed, indicating that the STO frequency responds to the change in magnetic field in less than 1 ns.

https://iopscience.iop.org/article/10.1143/APEX.4.013003/pdf

Real-Time Measurement of Temporal Response of a Spin-Torque Oscillator to Magnetic Pulses

The nonlinear frequency shift coefficient, which represents the strength of the transformation of amplitude fluctuations into phase fluctuations of an oscillator, is measured for MgO-based spin-torque oscillators by analyzing the current dependence of the power spectrum. We have observed that linewidth against inverse normalized power plots show linear behavior below and above the oscillation threshold as predicted by the analytical theories for spin-torque oscillators. The magnitude of the coefficient is determined from the ratio of the linear slopes. Small magnitude of the coefficient (∼3) has been obtained for the device exhibiting narrow linewidth (∼10 MHz) at high bias current.

Measurement of nonlinear frequency shift coefficient in spin-torque oscillators based on MgO tunnel junctions

Reservoir computing (a framework for machine learning) implemented in physical systems has attracted much attention for real-time computing in $e.g.$ time-series modeling and prediction, for which a single spin-torque oscillator (STO) has been proposed. The performance of a single STO will be limited, though. To solve this issue, the authors study reservoir computing on an $a\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}y$ of STOs, showing numerically that the system's performance can improve with more STOs, and can become remarkably better than for a standard neural-network model. Interestingly, performance is best near a boundary between synchronized and disordered states, suggesting an enhancement at the edge of chaos.

Tazumi Nagasawa

Papers

Spin-torque oscillator, magnetic head including the spin-torque oscillator, and magnetic recording and reproducing apparatus

Numerical Simulation on Temporal Response of Spin-Torque Oscillator to Magnetic Pulses

Real-Time Measurement of Temporal Response of a Spin-Torque Oscillator to Magnetic Pulses

Measurement of nonlinear frequency shift coefficient in spin-torque oscillators based on MgO tunnel junctions

Reservoir Computing on Spin-Torque Oscillator Array