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The brain naturally binds events from different sources in unique concepts. It is hypothesized that this process occurs through the transient mutual synchronization of neurons located in different regions of the brain when the stimulus is presented. This mechanism of 'binding through synchronization' can be directly implemented in neural networks composed of coupled oscillators. To do so, the oscillators must be able to mutually synchronize for the range of inputs corresponding to a single class, and otherwise remain desynchronized. Here we show that the outstanding ability of spintronic nano-oscillators to mutually synchronize and the possibility to precisely control the occurrence of mutual synchronization by tuning the oscillator frequencies over wide ranges allows pattern recognition. We demonstrate experimentally on a simple task that three spintronic nano-oscillators can bind consecutive events and thus recognize and distinguish temporal sequences. This work is a step forward in the construction of neural networks that exploit the non-linear dynamic properties of their components to perform brain-inspired computations. Spin-torque nano-oscillators have sparked interest for their potential in neuromorphic computing, however concrete demonstration are limited. Here, Romera et al show how spin-torque nano-oscillators can mutually synchronise and recognize temporal patterns, much like neurons, illustrating their potential for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Perpendicular magnetic anisotropy (PMA) is becoming increasingly important in spintronics research, especially for high-density magnetoresistive random access memories (MRAMs). The PMA induced at an Fe/MgO interface is widely used in magnetic tunnel junctions. Here, we propose inserting an ultrathin LiF layer at the interface in an epitaxial Fe/MgO junction. With a 0.3 nm-thick LiF layer, a large intrinsic interface PMA energy, Ki,0, of 2.8 mJ/m2 was achieved. We also found that the LiF/MgO bilayer tunneling barrier exhibited a large tunnel magnetoresistance (TMR) effect, suggesting that a coherent spin-dependent tunneling process was maintained in the ultrathin LiF layer. Atomic-scale interface engineering using fluoride can further improve the PMA and TMR properties of spintronic devices.We prepared fully epitaxial Fe/LiF/MgO/Fe magnetic tunnel junctions. A large enhancement of interface-induced perpendicular magnetic anisotropy was realized by introducing a few atomic layers of lithium fluoride (LiF) between Fe and MgO. Coherent spin-dependent tunneling through LiF/MgO bilayer tunneling barrier resulted in large tunnel magnetoresistance. Atomic-scale interface engineering using fluoride can pave a new way to improve the spintronic devices. [ABSTRACT FROM AUTHOR]

There is urgent need for spintronics materials exhibiting a large voltage modulation effect to fulfill the great demand for high-speed, low-power-consumption information processing systems. Fcc-Co (111)-based systems are a promising option for research on the voltage effect, on account of their large perpendicular magnetic anisotropy (PMA) and high degree of freedom in structure. Aiming to observe a large voltage effect in a fcc-Co (111)-based system at room temperature, we investigated the voltage-induced coercivity (Hc) change of perpendicularly magnetized Pt/heavy metal/Co/CoO/amorphous TiOx structures. The thin CoO layer in the structure was the result of the surface oxidation of Co. We observed a large voltage-induced Hc change of 20.2 mT by applying 2 V (0.32 V/nm) to a sample without heavy metal insertion, and an Hc change of 15.4 mT by applying 1.8 V (0.29 V/nm) to an Ir-inserted sample. The relative thick Co thickness, Co surface oxidation, and large dielectric constant of TiOx layer could be related to the large voltage-induced Hc change. Furthermore, we demonstrated the separate adjustment of Hc and a voltage-induced Hc change by utilizing both upper and lower interfaces of Co. [ABSTRACT FROM AUTHOR]

Converting charge current into spin current via the spin Hall effect enables efficient manipulation of magnetization by electrical current. However, its geometrical restriction is a serious obstacle to device applications because it prevents switching of perpendicular magnetization in the absence of an external field. To resolve this issue, ferromagnetic materials have attracted attentions because their time reversal asymmetry induces magnetic-dependent charge-to-spin conversion that removes this restriction. Here, we achieved a large enhancement of magnetic-dependent charge-to-spin conversion by clarifying its mechanism. Through layer thickness dependence of the conversion efficiency, we revealed a coexistence of interfacial and bulk contributions to the magnetic-dependent charge-to-spin conversion. Moreover, the interfacial contribution to charge-to-spin conversion is found to be dominant and can be controlled via interfacial band engineering. The efficiency of charge-to-spin conversion in ferromagnet was found to be an order larger than that of other materials with reduced symmetry. Geometrical restriction is a serious obstacle for electrical manipulation of magnetism using the spin Hall effect. Here, Hibino et al. report a mechanism of magnetic-dependent charge-to-spin conversion in a ferromagnet due to spin-orbit coupling, which removes this restriction and achieves a large enhancement of the conversion efficiency. [ABSTRACT FROM AUTHOR]

Neuromorphic computing using spintronic devices, such as spin-torque oscillators (STOs), has been intensively studied for energy-efficient data processing. One of the critical issues in this application is stochasticity in magnetization dynamics, which limits the accuracy of computation. Such stochastic behavior, however, plays a key role in stochastic computing and machine learning. It is therefore important to develop methods for both suppressing and enhancing stochastic response in spintronic devices. We report on experimental investigations on control of stochastic quantity, such as the width of a distribution of transient time in magnetization dynamics in vortex-type STO. The spin-transfer effect can suppress stochasticity in transient dynamics from a non-oscillating to oscillating state, whereas an application of a radio-frequency magnetic field is effective in reducing stochasticity on the time evolution of the oscillating state. [ABSTRACT FROM AUTHOR]

The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. This task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these transformations sometimes obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate bench-mark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators. [ABSTRACT FROM AUTHOR]

In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2-6, for solving complex problems with small networks7-11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12-16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators—that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field—can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization. A network of four spin-torque nano-oscillators can be trained in real time to recognize spoken vowels, in a simple and scalable approach that could be exploited for large-scale neural networks. [ABSTRACT FROM AUTHOR]

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behaviour to realize high-density, low-power neuromorphic computing will require very large numbers of nanoscale nonlinear oscillators. A simple estimation indicates that to fit 108 oscillators organized in a two-dimensional array inside a chip the size of a thumb, the lateral dimension of each oscillator must be smaller than one micrometre. However, nanoscale devices tend to be noisy and to lack the stability that is required to process data in a reliable way. For this reason, despite multiple theoretical proposals and several candidates, including memristive and superconducting oscillators, a proof of concept of neuromorphic computing using nanoscale oscillators has yet to be demonstrated. Here we show experimentally that a nanoscale spintronic oscillator (a magnetic tunnel junction) can be used to achieve spoken-digit recognition with an accuracy similar to that of state-of-the-art neural networks. We also determine the regime of magnetization dynamics that leads to the greatest performance. These results, combined with the ability of the spintronic oscillators to interact with each other, and their long lifetime and low energy consumption, open up a path to fast, parallel, on-chip computation based on networks of oscillators. [ABSTRACT FROM AUTHOR]

Using resonant phenomena to exert coherent control over electron spin dynamics could enable more-energy-efficient spintronic devices and related technologies. Yet, achieving collective spin resonant control by ferromagnetic resonance excitation most often requires radiofrequency magnetic fields or the injection of spin-polarized currents that consume relatively large amounts of power. Inducing ferromagnetic resonances directly with an electric field offers a lower-power alternative to these, but doing so requires strong electric and magnetic coupling that is difficult to realize owing to screening effects, particularly in metallic materials. Here, we demonstrate electric-field-induced ferromagnetic resonance excitation by means of voltage control over the magnetic anisotropy in a few monolayers of FeCo at room temperature. The technique provides a low-power, highly localized and coherent means to manipulate electron spin dynamics. [ABSTRACT FROM AUTHOR]

Spin-momentum transfer between a spin-polarized current and a ferromagnetic layer can induce steady-state magnetization precession, and has recently been proposed as a working principle for ubiquitous radio-frequency devices for radar and telecommunication applications. However, so far, the development of industrially attractive prototypes has been hampered by the inability to identify systems that can provide enough power. Here, we demonstrate that microwave signals with device-compatible output power levels can be generated from a single magnetic tunnel junction with a lateral size of 100 nm, seven orders of magnitude smaller than conventional radio-frequency oscillators. We find that in MgO magnetic tunnel junctions the perpendicular torque induced by the spin-polarized current on the local magnetization can reach 25% of the in-plane spin-torque term, although showing a different bias dependence. Both findings contrast with the results obtained on all-metallic structures, previously investigated, reflecting the fundamentally different transport mechanisms in the two types of structure. [ABSTRACT FROM AUTHOR]

When an electric current passes from one ferromagnetic layer via a non-magnetic layer into another ferromagnetic layer, the spin polarization and subsequent rotation of this current can induce a transfer of angular momentum that exerts a torque on the second ferromagnetic layer. This provides a potentially useful method to reverse and oscillate the magnetic momenta in nanoscale magnetic structures. Owing to the large current densities required to observe spin-torque-induced magnetization switching and microwave emission (∼107 A cm−2), accurately measuring the strength, or even the direction, of the associated spin torque has proved difficult. Yet, such measurements are crucial to refining our understanding of the mechanisms responsible and the theories that describe them. To address this, we present quantitative experimental measurements of the spin torque in MgO-based magnetic tunnel junctions for a wide range of bias currents covering the switching currents. The results verify the occurrence of two different spin-torque regimes with different bias dependences that agree well with theoretical predictions. [ABSTRACT FROM AUTHOR]

The tunnel magnetoresistance (TMR) effect in magnetic tunnel junctions (MTJs) is the key to developing magnetoresistive random-access-memory (MRAM), magnetic sensors and novel programmable logic devices. Conventional MTJs with an amorphous aluminium oxide tunnel barrier, which have been extensively studied for device applications, exhibit a magnetoresistance ratio up to 70% at room temperature. This low magnetoresistance seriously limits the feasibility of spintronics devices. Here, we report a giant MR ratio up to 180% at room temperature in single-crystal Fe/MgO/Fe MTJs. The origin of this enormous TMR effect is coherent spin-polarized tunnelling, where the symmetry of electron wave functions plays an important role. Moreover, we observed that their tunnel magnetoresistance oscillates as a function of tunnel barrier thickness, indicating that coherency of wave functions is conserved across the tunnel barrier. The coherent TMR effect is a key to making spintronic devices with novel quantum-mechanical functions, and to developing gigabit-scale MRAM. [ABSTRACT FROM AUTHOR]

The correlation of phase fluctuations in any type of oscillator fundamentally defines its spectral shape. However, in nonlinear oscillators, such as spin torque nano-oscillators, the frequency spectrum can become particularly complex. This is specifically true when not only considering thermal but also colored 1/f flicker noise processes, which are crucial in the context of the oscillator's long term stability. In this study, we address the frequency spectrum of spin torque oscillators in the regime of large-amplitude steady oscillations experimentally and as well theoretically. We particularly take both thermal and flicker noise into account. We perform a series of measurements of the phase noise and the spectrum on spin torque vortex oscillators, notably varying the measurement time duration. Furthermore, we develop the modelling of thermal and flicker noise in Thiele equation based simulations. We also derive the complete phase variance in the framework of the nonlinear auto-oscillator theory and deduce the actual frequency spectrum. We investigate its dependence on the measurement time duration and compare with the experimental results. Long term stability is important in several of the recent applicative developments of spin torque oscillators. This study brings some insights on how to better address this issue. [ABSTRACT FROM AUTHOR]