arXiv subjects

Paolo Bortolotti

Publications and source records attributed to Paolo Bortolotti.

At least 19 recordsLinked to original sources

arXiv2026

Reprogrammable magnonic logic in a multiferroic heterostructure via magnetoelectric coupling

The realization of fully reconfigurable, voltage-controlled, and programmable on-chip magnonic devices is essential to fully harness the potential of spin waves for signal processing, logic and neuromorphic computing. Yet, existing demonstrations of electrical tuning of magnonic responses are either volatile, current-driven and thus energy-inefficient, or rely on local strain modification limiting their scalability for wafer-scale integration. Here, we address this challenge using a BiFeO3/La0.67Sr0.33MnO3 multiferroic thin film heterostructure. We show that ferroelectric domain engineering in BiFeO3 enables deterministic tuning of the magnon dispersion of La0.67Sr0.33MnO3, producing frequency shifts up to $\sim 150 MHz$ and allowing reconfigurable waveguiding. Micro-focused Brillouin light scattering directly images these effects, revealing electrically defined magnonic waveguides and spatially programmable dispersion. Compared to conventional approaches, this method provides non-volatile and reversible control. Furthermore, using an inverse-design simulation code, we demonstrate the capability of our platform to perform advanced magnonic functions such as frequency demultiplexing. Our results open a new avenue for using magnetoelectric heterostructures for magnonic logic, with further applicability to reservoir and neuromorphic computing and AI driven magnonic devices.

cond-mat.mes-hall↗

arXiv2024

True amplification of spin waves in magnonic nano-waveguides

Magnonic nano-devices exploit magnons -- quanta of spin waves -- to transmit and process information within a single integrated platform that has the potential to outperform traditional semiconductor-based electronics for low power applications. The main missing cornerstone of this information nanotechnology is an efficient scheme for the direct amplification of propagating spin waves. The recent discovery of spin-orbit torque provided an elegant mechanism for propagation losses compensation. While partial compensation of the spin-wave damping has allowed for spin-wave signal modulation, true amplification - the exponential increase in the spin-wave intensity during propagation - has so far remained elusive. Here we evidence the operating conditions to achieve unambiguous amplification using clocked nanoseconds-long spin-orbit torque pulses in sub-micrometer wide magnonic waveguides, where the effective magnetization has been engineered to be close to zero to suppress the detrimental magnon-magnon scattering. As a result, we achieve an exponential increase in the intensity of propagating spin waves up to 500 % at a propagation distance of several micrometers. These results pave the way towards the implementation of energy efficient, cascadable magnonic architectures for wave-based information processing and complex on-chip computation.

cond-mat.mes-hall↗

arXiv2023

Magnetoelectric Coupling in Pb(Zr,Ti)O3/CoFeB Nanoscale Waveguides Studied by Propagating Spin-Wave Spectroscopy

This study introduces a method for the characterization of the magnetoelectric coupling in nanoscale Pb(Zr,Ti)O3/CoFeB thin film composites based on propagating spin-wave spectroscopy. Finite element simulations of the strain distribution in the devices indicated that the magnetoelastic effective field in the CoFeB waveguides was maximized in the Damon - Eshbach configuration. All-electrical broadband propagating spin-wave transmission measurements were conducted on Pb(Zr,Ti)O3/CoFeB magnetoelectric waveguides with lateral dimensions down to 700 nm. The results demonstrated that the spin-wave resonance frequency can be modulated by applying a bias voltage to Pb(Zr,Ti)O3. The modulation is hysteretic due to the ferroelastic behavior of Pb(Zr,Ti)O3. An analytical model was then used to correlate the change in resonance frequency to the induced magnetoelastic field in the magnetostrictive CoFeB waveguide. We observe a hysteresis magnetoelastic field strength with values as large as 5.61 mT, and a non-linear magnetoelectric coupling coefficient with a maximum value of 1.69 mT/V.

physics.app-ph↗

arXiv2023

Degenerate and non-degenerate parametric excitation in YIG nanostructures

We study experimentally the processes of parametric excitation in microscopic magnetically saturated disks of nanometer-thick Yttrium Iron Garnet. We show that, depending on the relative orientation between the parametric pumping field and the static magnetization, excitation of either degenerate or non-degenerate magnon pairs is possible. In the latter case, which is particularly important for applications associated with the realization of computation in the reciprocal space, a single-frequency pumping can generate pairs of magnons whose frequencies correspond to different eigenmodes of the disk. We show that, depending on the size of the disk and the modes involved, the frequency difference in a pair can vary in the range 0.1-0.8 GHz. We demonstrate that in this system, one can easily realize a practically important situation where several magnon pairs share the same mode. We also observe the simultaneous generation of up to six different modes using a fixed-frequency monochromatic pumping. Our experimental findings are supported by numerical calculations that allow us to unambiguously identify the excited modes. Our results open new possibilities for the implementation of reciprocal-space computing making use of low damping magnetic insulators.

cond-mat.mes-hall↗

arXiv2023

Non-hermiticity in spintronics: oscillation death in coupled spintronic nano-oscillators through emerging exceptional points

The emergence of exceptional points (EPs) in the parameter space of a non-hermitian (2D) eigenvalue problem is studied in a general sense in mathematical physics, and has in the last decade successively reached the scope of experiments. In coupled systems, it gives rise to unique physical phenomena, which enable novel approaches for the development of seminal types of highly sensitive sensors. Here, we demonstrate at room temperature the emergence of EPs in coupled spintronic nanoscale oscillators and hence exploit the system's non-hermiticity. We describe the observation of amplitude death of self-oscillations and other complex dynamics, and develop a linearized non-hermitian model of the coupled spintronic system, which properly describes the main experimental features. Interestingly, these spintronic nanoscale oscillators are deployment-ready in different applicational technologies, such as field, current or rotation sensors, radiofrequeny and wireless devices and, more recently, novel neuromorphic hardware solutions. Their unique and versatile properties, notably their large nonlinear behavior, open up unprecedented perspectives in experiments as well as in theory on the physics of exceptional points. Furthermore, the exploitation of EPs in spintronics devises a new paradigm for ultrasensitive nanoscale sensors and the implementation of complex dynamics in the framework of non-conventional computing.

cond-mat.mes-hall↗

arXiv2023

Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses

Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiples frequencies. Here we show that magnetic tunnel junctions can process analogue RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded radiofrequency artificial intelligence.

cond-mat.mes-hall↗

arXiv2023

Temperature-independent ferromagnetic resonance shift in Bi-doped YIG garnets through magnetic anisotropy tuning

Thin garnet films are becoming central for magnon-spintronics and spin-orbitronics devices as they show versatile magnetic properties together with low magnetic losses. These fields would benefit from materials in which heat does not affect the magnetization dynamics, an effect known as the non-linear thermal frequency shift. In this study, low damping Bi substituted Iron garnet (Bi:YIG) ultra-thin films have been grown using Pulsed Laser Deposition. Through a fine tuning of the growth parameters, the precise control of the perpendicular magnetic anisotropy allows to achieve a full compensation of the dipolar magnetic anisotropy. Strikingly, once the growth conditions are optimized, varying the growth temperature from 405 °C to 475 °C as the only tuning parameter induces the easy-axis to go from out-of-plane to in-plane. For films that are close to the dipolar compensation, Ferromagnetic Resonance measurements yield an effective magnetization $μ_{0}M_{eff} (T)$ that has almost no temperature dependence over a large temperature range (260 K to 400 K) resulting in an anisotropy temperature exponent of 2. These findings put Bi:YIG system among the very few materials in which the temperature dependence of the magnetic anisotropy varies at the same rate than the saturation magnetization. This interesting behavior is ascribed phenomenologically to the sizable orbital moment of $Bi^{3+}$.

cond-mat.mtrl-sci↗

arXiv2023

Complete identification of spin-wave eigenmodes excited by parametric pumping in YIG microdisks

We present the parametric excitation of spin-wave modes in YIG micro-disks via parallel pumping. Their spectroscopy is performed using magnetic resonance force microscopy (MRFM), while their spatial profiles are determined by micro-focus Brillouin light scattering (BLS). We observe that almost all the fundamental eigenmodes of an in-plane magnetized YIG micro-disk, calculated using a micromagnetic eigenmode solver, can be excited using the parallel pumping scheme, as opposed to the transverse one. The comparison between the MRFM and BLS data on one side, and the simulations on the other side, provides the complete spectroscopic labeling of over 40 parametrically excited modes. Our findings could be promising for spin-wave-based computation schemes, in which the amplitudes of a large number of spin-wave modes have to be controlled.

physics.app-ph↗

arXiv2022

Multilayer spintronic neural networks with radio-frequency connections

Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided that 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 radio frequency (RF) 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 RF 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 RF transmissions, without digitization, and consuming only a few milliwatts, which is a gain of more than four orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

cs.ET↗

arXiv2021

Stabilization of phase noise in spin torque nano oscillators by a phase locked loop

The main limitation in order to exploit spin torque nano-oscillators (STNOs) in various potential applications is their large phase noise. In this work, we demonstrate its efficient reduction by a highly reconfigurable, compact, specifically on-chip designed PLL based on custom integrated circuits. First, we thoroughly study the parameter space of the PLL+STNO system experimentally. Second, we present a theory which describes the locking of a STNO to an external signal in a general sense. In our developed theory, we do not restrict ourselves to the case of a perfect phase locking but also consider phase slips and the corresponding low offset frequency $1/f^2$ noise, so far the main drawback in such systems. Combining experiment and theory allows us to reveal complex parameter dependences of the system's phase noise. The results provide an important step for the optimization of noise properties and thus leverage the exploitation of STNOs in prospective real applications.

physics.app-ph↗

arXiv2021

Voltage-Controlled Reconfigurable Magnonic Crystal at the Submicron Scale

Multiferroics offer an elegant means to implement voltage-control and on the fly reconfigurability in microscopic, nanoscaled systems based on ferromagnetic materials. These properties are particularly interesting for the field of magnonics, where spin waves are used to perform advanced logical or analogue functions. Recently, the emergence of nano-magnonics {\color{black} is expected to} eventually lead to the large-scale integration of magnonic devices. However, a compact voltage-controlled, on demand reconfigurable magnonic system has yet to be shown. Here, we introduce the combination of multiferroics with ferromagnets in a fully epitaxial heterostructure to achieve such voltage-controlled and reconfigurable magnonic systems. Imprinting a remnant electrical polarization in thin multiferroic $\mathrm{BiFeO_3}$ with a periodicity of $500\,\mathrm{nm}$ yields a modulation of the effective magnetic field in the micron-scale, ferromagnetic $\mathrm{La_{2/3}Sr_{1/3}MnO_3}$ magnonic waveguide. We evidence the magneto-electrical coupling by characterizing the spin wave propagation spectrum in this artificial, voltage induced, magnonic crystal and demonstrate the occurrence of a robust magnonic bandgap with $>20 \,\mathrm{dB}$ rejection.

cond-mat.mes-hall↗

arXiv2021

Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the Multiply and Accumulate (MAC) operation, a key building block of neural networks, directly using analogue RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series we demonstrate the MAC operation and use it for classification of RF signals. These results open the path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.

cond-mat.dis-nn↗

arXiv2021

Flicker and random telegraph noise between gyrotropic and dynamic C-state of a vortex based spin torque nano oscillator

Vortex based spin torque nano oscillators (STVOs) can present more complex dynamics than the spin torque induced gyrotropic (G) motion of the vortex core. The respective dynamic modes and the transition between them can be controlled by experimental parameters such as the applied dc current. An interesting behavior is the stochastic transition from the G- to a dynamic C-state occurring for large current densities. Moreover, the C-state oscillations exhibit a constant active magnetic volume. We present noise measurements in the different dynamic states that allow accessing specific properties of the stochastic transition, such as the characteristic state transition frequency. Furthermore,we confirm, as theoretically predicted, an increase of flicker noise with $I_{dc}^2$ when the oscillation volume remains constant with the current. These results bring insight into the potential optimization of noise properties sought for many potential rf applications with spin torque oscillators. Furthermore, the investigated stochastic characteristics open up new potentialities, for instance in the emerging field of neuromorphic computing schemes.

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

Beyond the gyrotropic motion: dynamic C-state in vortex spin torque oscillators

In the present study, we investigate a dynamical mode beyond the gyrotropic (G) motion of a magnetic vortex core in a confined magnetic disk of a nano-pillar spin torque nano oscillator. It is characterized by the in-plane circular precession associated to a C-shaped magnetization distribution. We show a transition between G and C-state mode which is found to be purely stochastic in a current-controllable range. Supporting our experimental findings with micromagnetic simulations, we believe that the results provide novel opportunities for the dynamic and stochastic control of STOs, which could be interesting to be implemented for example in neuromorphic networks.

cond-mat.mes-hall↗

arXiv2020

Wireless communication between two magnetic tunnel junctions acting as oscillator and diode

Magnetic tunnel junctions are nanoscale spintronic devices with microwave generation and detection capabilities. Here we use the rectification effect called "spin-diode" in a magnetic tunnel junction to wirelessly detect the microwave emission of another junction in the auto-oscillatory regime. We show that the rectified spin-diode voltage measured at the receiving junction end can be reconstructed from the independently measured auto-oscillation and spin diode spectra in each junction. Finally we adapt the auto-oscillator model to the case of spin-torque oscillator and spin-torque diode and we show that accurately reproduces the experimentally observed features. These results will be useful to design circuits and chips based on spintronic nanodevices communicating through microwaves.

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↗