57 results on '"J. Grollier"'
Search Results
2. Neuromorphic spintronics
- Author
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J. Grollier, D. Querlioz, K. Y. Camsari, K. Everschor-Sitte, S. Fukami, M. D. Stiles, Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES), THALES-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS), Purdue University [West Lafayette], Institut für Physik [Mainz], Johannes Gutenberg - Universität Mainz (JGU), Tohoku University [Sendai], and National Institute of Standards and Technology [Gaithersburg] (NIST)
- Subjects
Quantitative Biology::Neurons and Cognition ,Neurosciences ,02 engineering and technology ,Physik (inkl. Astronomie) ,021001 nanoscience & nanotechnology ,Condensed Matter::Mesoscopic Systems and Quantum Hall Effect ,01 natural sciences ,Article ,Electronic, Optical and Magnetic Materials ,Computer Science::Emerging Technologies ,Affordable and Clean Energy ,0103 physical sciences ,Electrical and Electronic Engineering ,[PHYS.COND]Physics [physics]/Condensed Matter [cond-mat] ,010306 general physics ,0210 nano-technology ,Instrumentation ,ComputingMilieux_MISCELLANEOUS - Abstract
Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality. Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics. Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing. Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices.
- Published
- 2020
- Full Text
- View/download PDF
3. Neuromorphic computing with spintronic nanoscale oscillators
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J. Torrejon, M. Riou, F.A. Araujo, S. Tsunegi, G. Khalsa, D. Querlioz, P. Bortolotti, V. Cros, A. Fukushima, H. Kubota, S. Yuasa, M.D. Stiles, and J. Grollier
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Materials science ,Spintronics ,Neuromorphic engineering ,Nanotechnology ,Nanoscopic scale - Published
- 2017
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4. Mutual synchronization of spin torque nano-oscillators through a long-range and tunable electrical coupling scheme
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R, Lebrun, S, Tsunegi, P, Bortolotti, H, Kubota, A S, Jenkins, M, Romera, K, Yakushiji, A, Fukushima, J, Grollier, S, Yuasa, and V, Cros
- Subjects
Article - Abstract
The concept of spin-torque-driven high-frequency magnetization dynamics, allows the potential construction of complex networks of non-linear dynamical nanoscale systems, combining the field of spintronics and the study of non-linear systems. In the few previous demonstrations of synchronization of several spin-torque oscillators, the short-range nature of the magnetic coupling that was used has largely hampered a complete control of the synchronization process. Here we demonstrate the successful mutual synchronization of two spin-torque oscillators with a large separation distance through their long range self-emitted microwave currents. This leads to a strong improvement of both the emitted power and the linewidth. The full control of the synchronized state is achieved at the nanoscale through two active spin transfer torques, but also externally through an electrical delay line. These additional levels of control of the synchronization capability provide a new approach to develop spin-torque oscillator-based nanoscale microwave-devices going from microwave-sources to bio-inspired networks., The spintronics based complex network is promising for next generation computing systems but hampered by short-range spin-wave coupling. The authors make progress by achieving long range and tunable mutual synchronization of two spin-torque oscillators with improved emission power and signal linewidth.
- Published
- 2016
5. Applied Physics Letters 104, 052909 (2014)
- Author
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S. Boyn, S. Girod, V. Garcia, S. Fusil, S. Xavier, C. Deranlot, H. Yamada, C. Carrxe9txe9ro, E. Jacquet, M. Bibes, A.Barthxe9lxe9my, and J. Grollier
- Published
- 2014
6. ACS Nano 7, 5385 (2013)
- Author
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H. Yamada, V. Garcia, S. Fusil, S. Boyn, M. Marinova, A. Gloter, S. Xavier, J. Grollier, E. Jacquet, C. Carrxe9txe9ro, C. Deranlot, M. Bibes and A. Barthxe9lxe9my
- Published
- 2013
7. A ferroelectric memristor
- Author
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A. Chanthbouala, V. Garcia, R. O. Cherifi, K. Bouzehouane, S. Fusil, X. Moya, S. Xavier, H. Yamada, C. Deranlot, N. D. Mathur, M. Bibes, A. Barthxe9lxe9my and J. Grollier
- Published
- 2012
8. Unified description of bulk and interface-enhanced spin pumping
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C. H. van der Wal, S. M. Watts, B. J. van Wees, J. Grollier, Physics of Nanodevices, Surfaces and Thin Films, and Zernike Institute for Advanced Materials
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Physics ,Larmor precession ,Spin pumping ,Condensed Matter - Materials Science ,Spin polarization ,Condensed matter physics ,Condensed Matter - Mesoscale and Nanoscale Physics ,Relaxation (NMR) ,METAL INTERFACE ,General Physics and Astronomy ,Materials Science (cond-mat.mtrl-sci) ,FOS: Physical sciences ,RELAXATION ,Zero field splitting ,RESONANCE ,Mesoscale and Nanoscale Physics (cond-mat.mes-hall) ,Spinplasmonics ,Spin echo ,Spin Hall effect ,Condensed Matter::Strongly Correlated Electrons - Abstract
The dynamics of non-equilibrium spin accumulation generated in metals or semiconductors by rf magnetic field pumping is treated within a diffusive picture. The dc spin accumulation produced in a uniform system by a rotating applied magnetic field or by a precessing magnetization of a weak ferromagnet is in general given by a (small) fraction of hbar omega, where omega is the rotation or precession frequency. With the addition of a neighboring, field-free region and allowing for the diffusion of spins, the spin accumulation is dramatically enhanced at the interface, saturating at the universal value hbar omega in the limit of long spin relaxation time. This effect can be maximized when the system dimensions are of the order of sqrt(2pi D omega), where D is the diffusion constant. We compare our results to the interface spin pumping theory of A. Brataas et al. [Phys. Rev. B 66, 060404(R) (2002)].
- Published
- 2006
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9. Parametric excitation of magnetic vortex gyrations in spin-torque nano-oscillators
- Author
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Kay Yakushiji, A. Dussaux, Shinji Yuasa, Claudio Serpico, Rie Matsumoto, Paolo Bortolotti, Hitoshi Kubota, Akio Fukushima, Eva Grimaldi, Vincent Cros, Julie Grollier, P., Bortolotti, E., Grimaldi, A., Dussaux, J., Grollier, V., Cro, Serpico, Claudio, K., Yakushiji, A., Fukushima, H., Kubota, R., Matsumoto, and S., Yuasa
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Physics ,DYNAMICS ,Condensed matter physics ,POLARIZED CURRENT ,Natural frequency ,02 engineering and technology ,DRIVEN ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,01 natural sciences ,Electronic, Optical and Magnetic Materials ,Vortex ,Amplitude ,Excited state ,0103 physical sciences ,Torque ,010306 general physics ,0210 nano-technology ,Saturation (magnetic) ,Excitation ,Parametric statistics - Abstract
We experimentally demonstrate that large amplitude magnetic vortex gyrations can be parametrically excited by the injection of radio-frequency (rf) current at twice the natural frequency of the gyrotropic vortex-core motion. The mechanism of excitation is based on the parallel pumping of vortex motion by the rf orthoradial field generated by the injected current. Theoretical analysis shows that experimental results can be interpreted as the manifestation of parametric amplification when the rf current is small, and of parametric instability when the rf current is above a certain threshold. By taking into account the energy nonlinearities, we succeed to describe the amplitude saturation of vortex oscillations as well as the coexistence of stable regimes.
- Published
- 2013
10. Training an Ising machine with equilibrium propagation.
- Author
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Laydevant J, Marković D, and Grollier J
- Abstract
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine's connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications., (© 2024. The Author(s).)
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- 2024
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11. Multilayer spintronic neural networks with radiofrequency connections.
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Ross A, Leroux N, De Riz A, Marković D, Sanz-Hernández D, Trastoy J, Bortolotti P, Querlioz D, Martins L, Benetti L, Claro MS, Anacleto P, Schulman A, Taris T, Begueret JB, Saïghi S, Jenkins AS, Ferreira R, Vincent AF, Mizrahi FA, and Grollier J
- Abstract
Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided they implement state-of-the-art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radiofrequency signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly separable radiofrequency inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of-the-art identification of drones from their radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)
- Published
- 2023
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12. Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions.
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Farcis L, Teixeira BMS, Talatchian P, Salomoni D, Ebels U, Auffret S, Dieny B, Mizrahi FA, Grollier J, Sousa RC, and Buda-Prejbeanu LD
- Abstract
Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short- and long-term memory, nonlinear fast response, and relatively small footprint. Here we demonstrate experimentally how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions can emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two-terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic the neuron response in a dense neural network. The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks to sub-100 nm size elements.
- Published
- 2023
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13. Neuromorphic Engineering: From Materials to Device Application.
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Yang JJ, Grollier J, Williams RS, and Huang R
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- 2023
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14. Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations.
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Chen X, Araujo FA, Riou M, Torrejon J, Ravelosona D, Kang W, Zhao W, Grollier J, and Querlioz D
- Abstract
Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques have fallen short of discovering the full differential equation of an experimental physical system. Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them. For this purpose, we re-frame the formalism of Neural Ordinary Differential Equations to the constraints of spintronics: few measured outputs, multiple inputs and internal parameters. We demonstrate with Neural Ordinary Differential Equations an acceleration factor over 200 compared to micromagnetic simulations for a complex problem - the simulation of a reservoir computer made of magnetic skyrmions (20 minutes compared to three days). In a second realization, we show that we can predict the noisy response of experimental spintronic nano-oscillators to varying inputs after training Neural Ordinary Differential Equations on five milliseconds of their measured response to a different set of inputs. Neural Ordinary Differential Equations can therefore constitute a disruptive tool for developing spintronic applications in complement to micromagnetic simulations, which are time-consuming and cannot fit experiments when noise or imperfections are present. Our approach can also be generalized to other electronic devices involving dynamics., (© 2022. The Author(s).)
- Published
- 2022
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15. Binding events through the mutual synchronization of spintronic nano-neurons.
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Romera M, Talatchian P, Tsunegi S, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Cros V, Bortolotti P, Ernoult M, Querlioz D, and Grollier J
- Subjects
- Animals, Computer Simulation, Humans, Models, Neurological, Neurons physiology, Brain physiology, Cortical Synchronization physiology, Nerve Net physiology, Neural Networks, Computer
- Abstract
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., (© 2022. The Author(s).)
- Published
- 2022
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16. A quantum material spintronic resonator.
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Xu JW, Chen Y, Vargas NM, Salev P, Lapa PN, Trastoy J, Grollier J, Schuller IK, and Kent AD
- Abstract
In a spintronic resonator a radio-frequency signal excites spin dynamics that can be detected by the spin-diode effect. Such resonators are generally based on ferromagnetic metals and their responses to spin torques. New and richer functionalities can potentially be achieved with quantum materials, specifically with transition metal oxides that have phase transitions that can endow a spintronic resonator with hysteresis and memory. Here we present the spin torque ferromagnetic resonance characteristics of a hybrid metal-insulator-transition oxide/ ferromagnetic metal nanoconstriction. Our samples incorporate [Formula: see text], with Ni, Permalloy ([Formula: see text]) and Pt layers patterned into a nanoconstriction geometry. The first order phase transition in [Formula: see text] is shown to lead to systematic changes in the resonance response and hysteretic current control of the ferromagnetic resonance frequency. Further, the output signal can be systematically varied by locally changing the state of the [Formula: see text] with a dc current. These results demonstrate new spintronic resonator functionalities of interest for neuromorphic computing., (© 2021. The Author(s).)
- Published
- 2021
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17. Tunable Stochasticity in an Artificial Spin Network.
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Sanz-Hernández D, Massouras M, Reyren N, Rougemaille N, Schánilec V, Bouzehouane K, Hehn M, Canals B, Querlioz D, Grollier J, Montaigne F, and Lacour D
- Abstract
Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. This type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks., (© 2021 Wiley-VCH GmbH.)
- Published
- 2021
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18. EqSpike: spike-driven equilibrium propagation for neuromorphic implementations.
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Martin E, Ernoult M, Laydevant J, Li S, Querlioz D, Petrisor T, and Grollier J
- Abstract
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)
- Published
- 2021
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19. Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing Its Gradient Estimator Bias.
- Author
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Laborieux A, Ernoult M, Scellier B, Bengio Y, Grollier J, and Querlioz D
- Abstract
Equilibrium Propagation is a biologically-inspired algorithm that trains convergent recurrent neural networks with a local learning rule. This approach constitutes a major lead to allow learning-capable neuromophic systems and comes with strong theoretical guarantees. Equilibrium propagation operates in two phases, during which the network is let to evolve freely and then "nudged" toward a target; the weights of the network are then updated based solely on the states of the neurons that they connect. The weight updates of Equilibrium Propagation have been shown mathematically to approach those provided by Backpropagation Through Time (BPTT), the mainstream approach to train recurrent neural networks, when nudging is performed with infinitely small strength. In practice, however, the standard implementation of Equilibrium Propagation does not scale to visual tasks harder than MNIST. In this work, we show that a bias in the gradient estimate of equilibrium propagation, inherent in the use of finite nudging, is responsible for this phenomenon and that canceling it allows training deep convolutional neural networks. We show that this bias can be greatly reduced by using symmetric nudging (a positive nudging and a negative one). We also generalize Equilibrium Propagation to the case of cross-entropy loss (by opposition to squared error). As a result of these advances, we are able to achieve a test error of 11.7% on CIFAR-10, which approaches the one achieved by BPTT and provides a major improvement with respect to the standard Equilibrium Propagation that gives 86% test error. We also apply these techniques to train an architecture with unidirectional forward and backward connections, yielding a 13.2% test error. These results highlight equilibrium propagation as a compelling biologically-plausible approach to compute error gradients in deep neuromorphic systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Laborieux, Ernoult, Scellier, Bengio, Grollier and Querlioz.)
- Published
- 2021
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20. Ultrafast Neuromorphic Dynamics Using Hidden Phases in the Prototype of Relaxor Ferroelectrics.
- Author
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Prosandeev S, Grollier J, Talbayev D, Dkhil B, and Bellaiche L
- Abstract
Materials possessing multiple states are promising to emulate synaptic and neuronic behaviors. Their operation frequency, typically in or below the GHz range, however, limits the speed of neuromorphic computing. Ultrafast THz electric field excitation has been employed to induce nonequilibrium states of matter, called hidden phases in oxides. One may wonder if there are systems for which THz pulses can generate neuronic and synaptic behavior, via the creation of hidden phases. Using atomistic simulations, we discover that relaxor ferroelectrics can emulate all the key neuronic and memristive synaptic features. Their occurrence originates from the activation of many hidden phases of polarization order, resulting from the response of nanoregions to THz pulses. Such phases further possess different dielectric constants, which is also promising for memcapacitor devices.
- Published
- 2021
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21. Influence of flicker noise and nonlinearity on the frequency spectrum of spin torque nano-oscillators.
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Wittrock S, Talatchian P, Tsunegi S, Crété D, Yakushiji K, Bortolotti P, Ebels U, Fukushima A, Kubota H, Yuasa S, Grollier J, Cibiel G, Galliou S, Rubiola E, and Cros V
- Abstract
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.
- Published
- 2020
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22. Role of non-linear data processing on speech recognition task in the framework of reservoir computing.
- Author
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Abreu Araujo F, Riou M, Torrejon J, Tsunegi S, Querlioz D, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Stiles MD, and Grollier J
- Abstract
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.
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- 2020
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23. Neuromorphic Spintronics.
- Author
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Grollier J, Querlioz D, Camsari KY, Everschor-Sitte K, Fukami S, and Stiles MD
- Abstract
Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality. Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics. Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing. Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices.
- Published
- 2020
- Full Text
- View/download PDF
24. Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines.
- Author
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Ernoult M, Grollier J, and Querlioz D
- Abstract
One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from data with limited energy. For this purpose, memristive devices are excellent candidates to emulate synapses. A challenge, however, is to map existing learning algorithms onto a chip: for a physical implementation, a learning rule should ideally be tolerant to the typical intrinsic imperfections of such memristive devices, and local. Restricted Boltzmann Machines (RBM), for their local learning rule and inherent tolerance to stochasticity, comply with both of these constraints and constitute a highly attractive algorithm towards achieving memristor-based Deep Learning. On simulation grounds, this work gives insights into designing simple memristive devices programming protocols to train on chip Boltzmann Machines. Among other RBM-based neural networks, we advocate using a Discriminative RBM, with two hardware-oriented adaptations. We propose a pulse width selection scheme based on the sign of two successive weight updates, and show that it removes the constraint to precisely tune the initial programming pulse width as a hyperparameter. We also propose to evaluate the weight update requested by the algorithm across several samples and stochastic realizations. We show that this strategy brings a partial immunity against the most severe memristive device imperfections such as the non-linearity and the stochasticity of the conductance updates, as well as device-to-device variability.
- Published
- 2019
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25. Temporal pattern recognition with delayed feedback spin-torque nano-oscillators.
- Author
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Riou M, Torrejon J, Garitaine B, Araujo FA, Bortolotti P, Cros V, Tsunegi S, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Querlioz D, Stiles MD, and Grollier J
- Abstract
The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field.
- Published
- 2019
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26. Vowel recognition with four coupled spin-torque nano-oscillators.
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Romera M, Talatchian P, Tsunegi S, Abreu Araujo F, Cros V, Bortolotti P, Trastoy J, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Ernoult M, Vodenicarevic D, Hirtzlin T, Locatelli N, Querlioz D, and Grollier J
- Abstract
In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence
1 . 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.- Published
- 2018
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27. Scaling up electrically synchronized spin torque oscillator networks.
- Author
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Tsunegi S, Taniguchi T, Lebrun R, Yakushiji K, Cros V, Grollier J, Fukushima A, Yuasa S, and Kubota H
- Abstract
Synchronized nonlinear oscillators networks are at the core of numerous families of applications including phased array wave generators and neuromorphic pattern matching systems. In these devices, stable synchronization between large numbers of nanoscale oscillators is a key issue that remains to be demonstrated. Here, we show experimentally that synchronized spin-torque oscillator networks can be scaled up. By increasing the number of synchronized oscillators up to eight, we obtain that the emitted power and the quality factor increase linearly with the number of oscillators. Even more importantly, we demonstrate that the stability of synchronization in time exceeds 1.6 milliseconds corresponding to 10
5 periods of oscillation. Our study demonstrates that spin-torque oscillators are suitable for applications based on synchronized networks of oscillators.- Published
- 2018
- Full Text
- View/download PDF
28. Neural-like computing with populations of superparamagnetic basis functions.
- Author
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Mizrahi A, Hirtzlin T, Fukushima A, Kubota H, Yuasa S, Grollier J, and Querlioz D
- Abstract
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 computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead 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.
- Published
- 2018
- Full Text
- View/download PDF
29. Overcoming device unreliability with continuous learning in a population coding based computing system.
- Author
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Mizrahi A, Grollier J, Querlioz D, and Stiles MD
- Abstract
The brain, which uses redundancy and continuous learning to overcome the unreliability of its components, provides a promising path to building computing systems that are robust to the unreliability of their constituent nanodevices. In this work, we illustrate this path by a computing system based on population coding with magnetic tunnel junctions that implement both neurons and synaptic weights. We show that equipping such a system with continuous learning enables it to recover from the loss of neurons and makes it possible to use unreliable synaptic weights ( i.e. low energy barrier magnetic memories). There is a tradeoff between power consumption and precision because low energy barrier memories consume less energy than high barrier ones. For a given precision, there is an optimal number of neurons and an optimal energy barrier for the weights that leads to minimum power consumption.
- Published
- 2018
- Full Text
- View/download PDF
30. Neuromorphic computing with nanoscale spintronic oscillators.
- Author
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Torrejon J, Riou M, Araujo FA, Tsunegi S, Khalsa G, Querlioz D, Bortolotti P, Cros V, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Stiles MD, and Grollier J
- Abstract
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 10
8 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.- Published
- 2017
- Full Text
- View/download PDF
31. Probing Phase Coupling Between Two Spin-Torque Nano-Oscillators with an External Source.
- Author
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Li Y, de Milly X, Abreu Araujo F, Klein O, Cros V, Grollier J, and de Loubens G
- Abstract
Phase coupling between auto-oscillators is central for achieving coherent responses such as synchronization. Here we present an experimental approach to probe it in the case of two dipolarly coupled spin-torque vortex nano-oscillators using an external microwave field. By phase locking one oscillator to the external source, we observe frequency pulling on the second oscillator. From coupled phase equations we show analytically that this frequency pulling results from concerted actions of oscillator-oscillator and source-oscillator couplings. The analysis allows us to determine the strength and phase shift of coupling between two oscillators, yielding important information for the implementation of large interacting oscillator networks.
- Published
- 2017
- Full Text
- View/download PDF
32. Learning through ferroelectric domain dynamics in solid-state synapses.
- Author
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Boyn S, Grollier J, Lecerf G, Xu B, Locatelli N, Fusil S, Girod S, Carrétéro C, Garcia K, Xavier S, Tomas J, Bellaiche L, Bibes M, Barthélémy A, Saïghi S, and Garcia V
- Subjects
- Time Factors, Electricity, Iron chemistry, Neural Networks, Computer
- Abstract
In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.
- Published
- 2017
- Full Text
- View/download PDF
33. Interface-Induced Phenomena in Magnetism.
- Author
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Hellman F, Hoffmann A, Tserkovnyak Y, Beach GSD, Fullerton EE, Leighton C, MacDonald AH, Ralph DC, Arena DA, Dürr HA, Fischer P, Grollier J, Heremans JP, Jungwirth T, Kimel AV, Koopmans B, Krivorotov IN, May SJ, Petford-Long AK, Rondinelli JM, Samarth N, Schuller IK, Slavin AN, Stiles MD, Tchernyshyov O, Thiaville A, and Zink BL
- Abstract
This article reviews static and dynamic interfacial effects in magnetism, focusing on interfacially-driven magnetic effects and phenomena associated with spin-orbit coupling and intrinsic symmetry breaking at interfaces. It provides a historical background and literature survey, but focuses on recent progress, identifying the most exciting new scientific results and pointing to promising future research directions. It starts with an introduction and overview of how basic magnetic properties are affected by interfaces, then turns to a discussion of charge and spin transport through and near interfaces and how these can be used to control the properties of the magnetic layer. Important concepts include spin accumulation, spin currents, spin transfer torque, and spin pumping. An overview is provided to the current state of knowledge and existing review literature on interfacial effects such as exchange bias, exchange spring magnets, spin Hall effect, oxide heterostructures, and topological insulators. The article highlights recent discoveries of interface-induced magnetism and non-collinear spin textures, non-linear dynamics including spin torque transfer and magnetization reversal induced by interfaces, and interfacial effects in ultrafast magnetization processes.
- Published
- 2017
- Full Text
- View/download PDF
34. A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network.
- Author
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Vodenicarevic D, Locatelli N, Abreu Araujo F, Grollier J, and Querlioz D
- Abstract
With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices' non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing.
- Published
- 2017
- Full Text
- View/download PDF
35. Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator.
- Author
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Riou M, Araujo FA, Torrejon J, Tsunegi S, Khalsa G, Querlioz D, Bortolotti P, Cros V, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Stiles MD, and Grollier J
- Abstract
Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing their basic units: synapses and neurons. The challenge for neurons is to scale them down to submicrometer diameters while maintaining the properties that allow for reliable information processing: high signal to noise ratio, endurance, stability, reproducibility. In this work, we show that compact spin-torque nano-oscillators can naturally implement such neurons, and quantify their ability to realize an actual cognitive task. In particular, we show that they can naturally implement reservoir computing with high performance and detail the recipes for this capability.
- Published
- 2017
- Full Text
- View/download PDF
36. Spintronic Nanodevices for Bioinspired Computing.
- Author
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Grollier J, Querlioz D, and Stiles MD
- Abstract
Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal-oxide-semiconductor (CMOS) bioinspired hardware.
- Published
- 2016
- Full Text
- View/download PDF
37. A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy.
- Author
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Lequeux S, Sampaio J, Cros V, Yakushiji K, Fukushima A, Matsumoto R, Kubota H, Yuasa S, and Grollier J
- Abstract
Memristors are non-volatile nano-resistors which resistance can be tuned by applied currents or voltages and set to a large number of levels. 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 multilevel memristor compatible with spin-torque magnetic random access memories. 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.
- Published
- 2016
- Full Text
- View/download PDF
38. Controlling the phase locking of stochastic magnetic bits for ultra-low power computation.
- Author
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Mizrahi A, Locatelli N, Lebrun R, Cros V, Fukushima A, Kubota H, Yuasa S, Querlioz D, and Grollier J
- Abstract
When fabricating magnetic memories, one of the main challenges is to maintain the bit stability while downscaling. Indeed, for magnetic volumes of a few thousand nm(3), the energy barrier between magnetic configurations becomes comparable to the thermal energy at room temperature. Then, switches of the magnetization spontaneously occur. These volatile, superparamagnetic nanomagnets are generally considered useless. But what if we could use them as low power computational building blocks? Remarkably, they can oscillate without the need of any external dc drive, and despite their stochastic nature, they can beat in unison with an external periodic signal. Here we show that the phase locking of superparamagnetic tunnel junctions can be induced and suppressed by electrical noise injection. We develop a comprehensive model giving the conditions for synchronization, and predict that it can be achieved with a total energy cost lower than 10(-13) J. Our results open the path to ultra-low power computation based on the controlled synchronization of oscillators.
- Published
- 2016
- Full Text
- View/download PDF
39. Self-Injection Locking of a Vortex Spin Torque Oscillator by Delayed Feedback.
- Author
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Tsunegi S, Grimaldi E, Lebrun R, Kubota H, Jenkins AS, Yakushiji K, Fukushima A, Bortolotti P, Grollier J, Yuasa S, and Cros V
- Abstract
The self-synchronization of spin torque oscillators is investigated experimentally by re-injecting its radiofrequency (rf) current after a certain delay time. We demonstrate that the integrated power and spectral linewidth are improved for optimal delays. Moreover by varying the phase difference between the emitted power and the re-injected one, we find a clear oscillatory dependence on the phase difference with a 2π periodicity of the frequency of the oscillator as well as its power and linewidth. Such periodical behavior within the self-injection regime is well described by the general model of nonlinear auto-oscillators including not only a delayed rf current but also all spin torque forces responsible for the self-synchronization. Our results reveal new approaches for controlling the non-autonomous dynamics of spin torque oscillators, a key issue for rf spintronics applications as well as for the development of neuro-inspired spin-torque oscillators based devices.
- Published
- 2016
- Full Text
- View/download PDF
40. Spin-transfer torque in ferromagnetic bilayers generated by anomalous Hall effect and anisotropic magnetoresistance.
- Author
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Taniguchi T, Grollier J, and Stiles MD
- Abstract
We propose an experimental scheme to determine the spin-transfer torque efficiency excited by the spin-orbit interaction in ferromagnetic bilayers from the measurement of the longitudinal magnetoresistace. Solving a diffusive spin-transport theory with appropriate boundary conditions gives an analytical formula of the longitudinal charge current density. The longitudinal charge current has a term that is proportional to the square of the spin-transfer torque efficiency and that also depends on the ratio of the film thickness to the spin diffusion length of the ferromagnet. Extracting this contribution from measurements of the longitudinal resistivity as a function of the thickness can give the spin-transfer torque efficiency.
- Published
- 2016
- Full Text
- View/download PDF
41. Efficient Synchronization of Dipolarly Coupled Vortex-Based Spin Transfer Nano-Oscillators.
- Author
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Locatelli N, Hamadeh A, Abreu Araujo F, Belanovsky AD, Skirdkov PN, Lebrun R, Naletov VV, Zvezdin KA, Muñoz M, Grollier J, Klein O, Cros V, and de Loubens G
- Abstract
Due to their nonlinear properties, spin transfer nano-oscillators can easily adapt their frequency to external stimuli. This makes them interesting model systems to study the effects of synchronization and brings some opportunities to improve their microwave characteristics in view of their applications in information and communication technologies and/or to design innovative computing architectures. So far, mutual synchronization of spin transfer nano-oscillators through propagating spinwaves and exchange coupling in a common magnetic layer has been demonstrated. Here we show that the dipolar interaction is also an efficient mechanism to synchronize neighbouring oscillators. We experimentally study a pair of vortex-based spin transfer nano-oscillators, in which mutual synchronization can be achieved despite a significant frequency mismatch between oscillators. Importantly, the coupling efficiency is controlled by the magnetic configuration of the vortices, as confirmed by an analytical model and micromagnetic simulations highlighting the physics at play in the synchronization process.
- Published
- 2015
- Full Text
- View/download PDF
42. Understanding of Phase Noise Squeezing Under Fractional Synchronization of a Nonlinear Spin Transfer Vortex Oscillator.
- Author
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Lebrun R, Jenkins A, Dussaux A, Locatelli N, Tsunegi S, Grimaldi E, Kubota H, Bortolotti P, Yakushiji K, Grollier J, Fukushima A, Yuasa S, and Cros V
- Abstract
We investigate experimentally the synchronization of vortex based spin transfer nano-oscillators to an external rf current whose frequency is at multiple integers, as well as at an integer fraction, of the oscillator frequency. Through a theoretical study of the locking mechanism, we highlight the crucial role of both the symmetries of the spin torques and the nonlinear properties of the oscillator in understanding the phase locking mechanism. In the locking regime, we report a phase noise reduction down to -90 dBc/Hz at 1 kHz offset frequency. Our demonstration that the phase noise of these nanoscale nonlinear oscillators can be tuned and eventually lessened, represents a key achievement for targeted radio frequency applications using spin torque devices.
- Published
- 2015
- Full Text
- View/download PDF
43. Plasticity in memristive devices for spiking neural networks.
- Author
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Saïghi S, Mayr CG, Serrano-Gotarredona T, Schmidt H, Lecerf G, Tomas J, Grollier J, Boyn S, Vincent AF, Querlioz D, La Barbera S, Alibart F, Vuillaume D, Bichler O, Gamrat C, and Linares-Barranco B
- Abstract
Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.
- Published
- 2015
- Full Text
- View/download PDF
44. Origin of spectral purity and tuning sensitivity in a spin transfer vortex nano-oscillator.
- Author
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Hamadeh A, Locatelli N, Naletov VV, Lebrun R, de Loubens G, Grollier J, Klein O, and Cros V
- Abstract
We investigate the microwave characteristics of a spin transfer nano-oscillator (STNO) based on coupled vortices as a function of the perpendicular magnetic field H(⊥). Interestingly, we find that our vortex-based oscillator is quasi-isochronous independently of H(⊥) and for a dc current ranging between 18 and 25 mA. It means that the severe nonlinear broadening usually observed in STNOs can be suppressed on a broad range of bias. Still, the generation linewidth displays strong variations on H(⊥) (from 40 kHz to 1 MHz), while the frequency tunability in current remains almost constant (7 MHz/mA). This demonstrates that isochronicity does not necessarily imply a loss of frequency tunability, which is here governed by the current induced Oersted field. It is not sufficient either to achieve the highest spectral purity in the full range of H(⊥). We show that the observed linewidth broadenings are due to the excited mode interacting with a lower energy overdamped mode, which occurs at the successive crossings between harmonics of these two modes. These findings open new possibilities for the design of STNOs and the optimization of their performance.
- Published
- 2014
- Full Text
- View/download PDF
45. Spin-torque building blocks.
- Author
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Locatelli N, Cros V, and Grollier J
- Abstract
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.
- Published
- 2014
- Full Text
- View/download PDF
46. Giant electroresistance of super-tetragonal BiFeO3-based ferroelectric tunnel junctions.
- Author
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Yamada H, Garcia V, Fusil S, Boyn S, Marinova M, Gloter A, Xavier S, Grollier J, Jacquet E, Carrétéro C, Deranlot C, Bibes M, and Barthélémy A
- Abstract
Ferroelectric tunnel junctions enable a nondestructive readout of the ferroelectric state via a change of resistance induced by switching the ferroelectric polarization. We fabricated submicrometer solid-state ferroelectric tunnel junctions based on a recently discovered polymorph of BiFeO3 with giant axial ratio ("T-phase"). Applying voltage pulses to the junctions leads to the highest resistance changes (OFF/ON ratio >10,000) ever reported with ferroelectric tunnel junctions. Along with the good retention properties, this giant effect reinforces the interest in nonvolatile memories based on ferroelectric tunnel junctions. We also show that the changes in resistance scale with the nucleation and growth of ferroelectric domains in the ultrathin BiFeO3 (imaged by piezoresponse force microscopy), thereby suggesting potential as multilevel memory cells and memristors.
- Published
- 2013
- Full Text
- View/download PDF
47. Isolating the dynamic dipolar interaction between a pair of nanoscale ferromagnetic disks.
- Author
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Keatley PS, Gangmei P, Dvornik M, Hicken RJ, Grollier J, and Ulysse C
- Abstract
Dynamic dipolar interactions between spin wave eigenmodes of closely spaced nanomagnets determine the collective behavior of magnonic and spintronic metamaterials and devices. However, dynamic dipolar interactions are difficult to quantify since their effects must be disentangled from those of static dipolar interactions and variations in the shape, size, and magnetic properties of the nanomagnets. It is shown that when two imperfect nanoscale magnetic disks with similar but nonidentical modes are brought into close proximity, the effect of the dynamic dipolar interaction can be detected by considering the difference of the phase of precession within the two disks. Measurements show that the interaction is stronger than expected from micromagnetic simulations, highlighting both the need for characterization and control of magnetic properties at the deep nanoscale, and also the potential for improved control of collective magnetic phenomena. Our approach is equally applicable to other physical systems in which dynamic interactions are obscured by inhomogeneous broadening and static interactions.
- Published
- 2013
- Full Text
- View/download PDF
48. High domain wall velocities via spin transfer torque using vertical current injection.
- Author
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Metaxas PJ, Sampaio J, Chanthbouala A, Matsumoto R, Anane A, Fert A, Zvezdin KA, Yakushiji K, Kubota H, Fukushima A, Yuasa S, Nishimura K, Nagamine Y, Maehara H, Tsunekawa K, Cros V, and Grollier J
- Subjects
- Computer Simulation, Information Storage and Retrieval, Spin Labels, Magnetics, Magnets chemistry, Nanotechnology, Torque
- Abstract
Domain walls, nanoscale transition regions separating oppositely oriented ferromagnetic domains, have significant promise for use in spintronic devices for data storage and memristive applications. The state of these devices is related to the wall position and thus rapid operation will require a controllable onset of domain wall motion and high speed wall displacement. These processes are traditionally driven by spin transfer torque due to lateral injection of spin polarized current through a ferromagnetic nanostrip. However, this geometry is often hampered by low maximum wall velocities and/or a need for prohibitively high current densities. Here, using time-resolved magnetotransport measurements, we show that vertical injection of spin currents through a magnetic tunnel junction can drive domain walls over hundreds of nanometers at ~500 m/s using current densities on the order of 6 MA/cm(2). Moreover, these measurements provide information about the stochastic and deterministic aspects of current driven domain wall mediated switching.
- Published
- 2013
- Full Text
- View/download PDF
49. A ferroelectric memristor.
- Author
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Chanthbouala A, Garcia V, Cherifi RO, Bouzehouane K, Fusil S, Moya X, Xavier S, Yamada H, Deranlot C, Mathur ND, Bibes M, Barthélémy A, and Grollier J
- Abstract
Memristors are continuously tunable resistors that emulate biological synapses. Conceptualized in the 1970s, they traditionally operate by voltage-induced displacements of matter, although the details of the mechanism remain under debate. Purely electronic memristors based on well-established physical phenomena with albeit modest resistance changes have also emerged. Here we demonstrate that voltage-controlled domain configurations in ferroelectric tunnel barriers yield memristive behaviour with resistance variations exceeding two orders of magnitude and a 10 ns operation speed. Using models of ferroelectric-domain nucleation and growth, we explain the quasi-continuous resistance variations and derive a simple analytical expression for the memristive effect. Our results suggest new opportunities for ferroelectrics as the hardware basis of future neuromorphic computational architectures.
- Published
- 2012
- Full Text
- View/download PDF
50. Solid-state memories based on ferroelectric tunnel junctions.
- Author
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Chanthbouala A, Crassous A, Garcia V, Bouzehouane K, Fusil S, Moya X, Allibe J, Dlubak B, Grollier J, Xavier S, Deranlot C, Moshar A, Proksch R, Mathur ND, Bibes M, and Barthélémy A
- Subjects
- Information Storage and Retrieval, Microscopy, Atomic Force, Nanotechnology instrumentation, Nanotechnology methods, Optical Storage Devices, Electromagnetic Fields, Magnets chemistry, Nanostructures chemistry
- Abstract
Ferroic-order parameters are useful as state variables in non-volatile information storage media because they show a hysteretic dependence on their electric or magnetic field. Coupling ferroics with quantum-mechanical tunnelling allows a simple and fast readout of the stored information through the influence of ferroic orders on the tunnel current. For example, data in magnetic random-access memories are stored in the relative alignment of two ferromagnetic electrodes separated by a non-magnetic tunnel barrier, and data readout is accomplished by a tunnel current measurement. However, such devices based on tunnel magnetoresistance typically exhibit OFF/ON ratios of less than 4, and require high powers for write operations (>1 × 10(6) A cm(-2)). Here, we report non-volatile memories with OFF/ON ratios as high as 100 and write powers as low as ∼1 × 10(4) A cm(-2) at room temperature by storing data in the electric polarization direction of a ferroelectric tunnel barrier. The junctions show large, stable, reproducible and reliable tunnel electroresistance, with resistance switching occurring at the coercive voltage of ferroelectric switching. These ferroelectric devices emerge as an alternative to other resistive memories, and have the advantage of not being based on voltage-induced migration of matter at the nanoscale, but on a purely electronic mechanism.
- Published
- 2011
- Full Text
- View/download PDF
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