Nicolas Locatelli

Spin-torque building blocks

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 devices shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.

Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems

Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic and industrial development, since it features non-volatility, high write and read speed and high endurance. In this work, we show that when used in a non-conventional regime, it can additionally act as a stochastic memristive device, appropriate to implement a “synaptic” function. We introduce basic concepts relating to spin-transfer torque magnetic tunnel junction (STT-MTJ, the STT-MRAM cell) behavior and its possible use to implement learning-capable synapses. Three programming regimes (low, intermediate and high current) are identified and compared. System-level simulations on a task of vehicle counting highlight the potential of the technology for learning systems. Monte Carlo simulations show its robustness to device variations. The simulations also allow comparing system operation when the different programming regimes of STT-MTJs are used. In comparison to the high and low current regimes, the intermediate current regime allows minimization of energy consumption, while retaining a high robustness to device variations. These results open the way for unexplored applications of STT-MTJs in robust, low power, cognitive-type systems.

Phase locking dynamics of dipolarly coupled vortex-based spin transfer oscillators

Phase locking dynamics of dipolarly coupled vortices excited by spin-polarized current in two identical nanopillars is studied as a function of the interpillar distance L. Numerical study and an analytical model have proved the remarkable efficiency of magnetostatic interaction in achieving phase locking. Investigating the dynamics in the transient regime toward phase locking, we extract the evolution of the locking time τ , the coupling strength μ, and the interaction energy W. Finally, we compare this coupling energy with the one obtained by a simple model.

Learning through ferroelectric domain dynamics in solid-state synapses

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.

Efficient Synchronization of Dipolarly Coupled Vortex-Based Spin Transfer Nano-Oscillators.

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.

Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.

Numerical and analytical investigation of the synchronization of dipolarly coupled vortex spin-torque nano-oscillators

We investigate analytically and numerically the synchronization dynamics of dipolarly coupled vortex based Spin-Torque Nano Oscillators with different pillar diameters. We identify the critical interpillar distances on which synchronization occurs as a function of their diameter mismatch. We obtain numerically a phase diagram showing the transition between unsynchronized and synchronized states and compare it to analytical predictions we make using the Thiele approach. Our study demonstrates that for relatively small diameter differences the synchronization dynamics can be described qualitatively using Adler equation. However, when the diameters difference increases significantly, the system becomes strongly non-Adlerian.

Identification and selection rules of the spin-wave eigen-modes in a normally magnetized nano-pillar

We report on a spectroscopic study of the spin-wave eigen-modes inside an individual normally magnetized two layers circular nano-pillar (Permalloy

Spintronic devices as key elements for energy-efficient neuroinspired architectures

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Perfect and robust phase-locking of a spin transfer vortex nano-oscillator to an external microwave source

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