arXiv subjects

Akio Fukushima

Publications and source records attributed to Akio Fukushima.

At least 19 recordsLinked to original sources

arXiv2024

Magnetization dynamics driven by displacement currents across a magnetic tunnel junction

Understanding the high-frequency transport characteristics of magnetic tunnel junctions (MTJs) is crucial for the development of fast-operating spintronics memories and radio frequency devices. Here, we present the study of frequency-dependent capacitive current effect in CoFeB/MgO-based MTJs and its influence on magnetization dynamics using time-resolved magneto-optical Kerr effect technique. In our device operating at gigahertz frequencies, we find a large displacement current of the order of mA's, which does not break the tunnel barrier of the MTJ. Importantly, this current generates an Oersted field and spin-orbit torque, inducing magnetization dynamics. Our discovery holds promise for building robust MTJ devices operating under high current conditions, also highlighting the significance of capacitive impedance in high frequency magnetotransport techniques.

cond-mat.mes-hall↗

arXiv2020

Spin-torque Dynamics for Noise Reduction in Vortex-based Sensors

Performance of magnetoresistive sensors is today mainly limited by their 1/f low-frequency noise. Here, we study this noise component in vortex-based TMR sensors. We compare the noise level in different magnetization configurations of the device, i.e vortex state or uniform parallel or antiparallel states. We find that the vortex state is at least an order of magnitude noisier than the uniform states. Nevertheless, by activating the spin-transfer induced dynamics of the vortex configuration, we observe a reduction of the 1/f noise, close to the values measured in the AP state, as the vortex core has a lower probability of pinning into defect sites. Additionally, by driving the dynamics of the vortex core by a non-resonant rf field or current we demonstrate that the 1/f noise can be further decreased. The ability to reduce the 1/f low-frequency noise in vortex-based devices by leveraging their spin-transfer dynamics thus enhances their applicability in the magnetic sensors' landscape.

cond-mat.mes-hall↗

arXiv2020

Influence of flicker noise and nonlinearity on the frequency spectrum of spin torque nano-oscillators

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.

cond-mat.mes-hall↗

arXiv2020

Binding events through the mutual synchronization of spintronic nano-neurons

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.

physics.app-ph↗

arXiv2019

Role of non-linear data processing on speech recognition task in the framework of reservoir computing

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. However, 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 may 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 benchmark 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.

eess.AS↗

arXiv2019

Low offset frequency $1/f$ flicker noise in spin torque vortex oscillators

Low frequency noise close to the carrier remains little explored in spin torque nano oscillators. However, it is crucial to investigate as it limits the oscillator's frequency stability. This work addresses the low offset frequency flicker noise of a TMR-based spin-torque vortex oscillator in the regime of large amplitude steady oscillations. We first phenomenologically expand the nonlinear auto-oscillator theory aiming to reveal the properties of this noise. We then present a thorough experimental study of the oscillator's $1/f$ flicker noise and discuss the results based on the theoretical predictions. Hereby, we connect the oscillator's nonlinear dynamics with the concept of flicker noise and furthermore refer to the influence of a standard $1/f$ noise description based on the Hooge formula, taking into account the non-constant magnetic oscillation volume, which contributes to the magnetoresistance.

cond-mat.mes-hall↗

arXiv2018

Symmetry and polarity of the voltage-controlled magnetic anisotropy studied by the Anomalous Hall effect

The voltage-controlled magnetic anisotropy (VCMA) effect in FeB and FeB/W films was measured by four independent methods. All measurements are consistent and show the same tendency. The coercive field, Hall angle, anisotropy field, the magnetization switching time and retention time linearly decrease when the gate voltage increases and they linearly increase when the gate voltage decreases.

cond-mat.mtrl-sci↗

arXiv2018

Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators

Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here we show that the frequency and the phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way we considerably decrease amplitude, phase and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features.

physics.app-ph↗

arXiv2018

Vowel recognition with four coupled spin-torque nano-oscillators

Substantial evidence indicates that the brain uses principles of non-linear dynamics in neural processes, providing inspiration for computing with nanoelectronic devices. However, training neural networks composed of dynamical nanodevices requires finely controlling and tuning their coupled oscillations. In this work, we show that the outstanding tunability of spintronic nano-oscillators can solve 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 high frequency tunability of the oscillators and their mutual coupling. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with non-linear dynamical features: here, oscillations and synchronization. This demonstration is a milestone for spintronics-based neuromorphic computing.

q-bio.NC↗

arXiv2018

Tunnel magnetoresistance angular and bias dependence enabling tuneable wireless communication

Spin-transfer torques (STTs) can be exploited in order to manipulate the magnetic moments of nanomagnets, thus allowing for new consumer-oriented devices to be designed. Of particular interest here are tuneable radio-frequency (RF) oscillators for wireless communication. Currently, the structure that maximizes the output power is an Fe/MgO/Fe-type magnetic tunnel junction (MTJ) with a fixed layer magnetized in the plane of the layers and a free layer magnetized perpendicular to the plane. This structure allows for most of the tunnel magnetoresistance (TMR) to be converted into output power. Here, we experimentally and theoretically demonstrate that the main mechanism sustaining steady-state precession in such structures is the angular dependence of the magnetoresistance. The TMR of such devices is known to exhibit a broken-linear dependence versus the applied bias. Our results show that the TMR bias dependence effectively quenches spin-transfer-driven precession and introduces a non-monotonic frequency dependence at high applied currents. Thus we expect the bias dependence of the TMR to have an even more dramatic effect in MTJs with Mn-Ga-based free layers, which could be used to design wireless oscillators extending towards the THz gap, but have been experimentally shown to exhibit a non-trivial TMR bias dependence.

physics.app-ph↗

arXiv2018

Neural-like computing with populations of superparamagnetic basis functions

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for calculating. For this purpose, the responses of the nanodevices should be non-linear, and each tuned to different values of the input. These strong requirements have prevented a demonstration of population coding with nanodevices. Here, we show that nanoscale magnetic tunnel junctions can be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.

cs.ET↗

arXiv2017

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is prohibitive for these computing schemes. In this paper, we introduce random number bit generation based on specific nanodevices: superparamagnetic tunnel junctions. We experimentally demonstrate high quality random bit generation that represents orders-of-magnitude improvements in energy efficiency compared to current solutions. We show that the random generation speed improves with nanodevice scaling, and investigate the impact of temperature, magnetic field and crosstalk. Finally, we show how alternative computing schemes can be implemented using superparamagentic tunnel junctions as random number generators. These results open the way for fabricating efficient hardware computing devices leveraging stochasticity, and highlight a novel use for emerging nanodevices.

physics.app-ph↗

arXiv2017

Neuromorphic computing with nanoscale spintronic oscillators

Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behavior to realize high density, low power neuromorphic computing will require huge numbers of nanoscale non-linear oscillators. Indeed, a simple estimation indicates that, in order to fit a hundred million oscillators organized in a two-dimensional array inside a chip the size of a thumb, their lateral dimensions must be smaller than one micrometer. However, despite multiple theoretical proposals, there is no proof of concept today of neuromorphic computing with nano-oscillators. Indeed, nanoscale devices tend to be noisy and to lack the stability required to process data in a reliable way. Here, we show experimentally that a nanoscale spintronic oscillator can achieve spoken digit recognition with accuracies similar to state of the art neural networks. We pinpoint the regime of magnetization dynamics leading to highest performance. These results, combined with the exceptional ability of these spintronic oscillators to interact together, their long lifetime, and low energy consumption, open the path to fast, parallel, on-chip computation based on networks of oscillators.

cs.ET↗

arXiv2016

Inside the perpendicular spin-torque memristor

Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a number of applications such as multilevel non-volatile memories and artificial nano-synapses, which are the focus of this work. A key point towards the development of large scale memristive neuromorphic hardware is to build these neural networks with a memristor technology compatible with the best candidates for the future mainstream non-volatile memories. Here we show the first experimental achievement of a memristor compatible with Spin-Torque Magnetic Random Access Memory. The resistive switching in our spin-torque memristor is linked to the displacement of a magnetic domain wall by spin-torques in a perpendicularly magnetized magnetic tunnel junction. We demonstrate that our magnetic synapse has a large number of intermediate resistance states, sufficient for neural computation. Moreover, we show that engineering the device geometry allows leveraging the most efficient spin torque to displace the magnetic domain wall at low current densities and thus to minimize the energy cost of our memristor. Our results pave the way for spin-torque based analog magnetic neural computation.

cond-mat.mes-hall↗

arXiv2016

Twist in the bias-dependence of spin-torques in magnetic tunnel junctions

The spin-torque in magnetic tunnel junctions possesses two components that both depend on the applied voltage. Here, we develop a new method for the accurate extraction of this bias-dependence from experiments over large voltage ranges. We study several junctions with different magnetic layer structures of the top electrode. Our results obtained on junctions with symmetric CoFeB electrodes agree well with theoretical calculations. The bias-dependences of asymmetric samples, with top electrodes containing NiFe, however, are twisted compared to the quadratic form generally assumed. Our measurements reveal the complexity of spin-torque mechanisms at large bias.

cond-mat.mes-hall↗

arXiv2015

Increased magnetic damping of a single domain wall and adjacent magnetic domains detected by spin torque diode in a nanostripe

We use spin-torque resonance to probe simultaneously and separately the dynamics of a magnetic domain wall and of magnetic domains in a nanostripe magnetic tunnel junction. Thanks to the large associated resistance variations we are able to analyze quantitatively the resonant properties of these single nanoscale magnetic objects. In particular, we find that the magnetic damping of both domains and domain walls is doubled compared to the damping value of their host magnetic layer. We estimate the contributions to damping arising from dipolar couplings between the different layers in the junction and from the intralayer spin pumping effect. We find that they cannot explain the large damping enhancement that we observe. We conclude that the measured increased damping is intrinsic to large amplitudes excitations of spatially localized modes or solitons such as vibrating or propagating domain walls

cond-mat.mtrl-sci↗

arXiv2014

Nonlinear behavior and mode coupling in spin transfer nano-oscillators

By investigating thoroughly the tunable behavior of coupled modes, we highlight how it provides new means to handle the properties of spin transfer nano-oscillators. We first demonstrate that the main features of the microwave signal associated with coupled vortex dynamics i.e. frequency, spectral coherence, critical current, mode localization, depends drastically on the relative vortex core polarities. Secondly we report a large reduction of the nonlinear linewidth broadening obtained by changing the effective damping through the control of the core configuration. Such a level of control on the nonlinear behavior reinforces our choice to exploit the microwave properties of collective modes for applications of spintronic devices in novel generation of integrated telecommunication devices.

cond-mat.mes-hall↗

arXiv2014

Noise-enhanced synchronization of stochastic magnetic oscillators

We present an experimental study of phase-locking in a stochastic magnetic oscillator. The system comprises a magnetic tunnel junction with a superparamagnetic free layer, whose magnetization dynamics is driven with spin torques through an external periodic driving current. We show that synchronization of this stochastic oscillator to the input current is possible for current densities below $3 \times 10^6$ A/cm$^2$, and occurs for input frequencies lower than the natural mean frequency of the stochastic oscillator. We show that such injection-locking is robust and leads to a drastic reduction in the phase diffusion of the stochastic oscillator, despite the presence of a frequency mismatch between the oscillator and the excitation.

cond-mat.mtrl-sci↗