arXiv subjects

Luana Benetti

Publications and source records attributed to Luana Benetti.

15 recordsLinked to original sources

arXiv2026

Deterministic control of the probabilistic phase dynamics in injection-locked spin-torque nano-oscillators

Spin-torque nano-oscillators (STNOs) inherently exhibit thermally driven phase fluctuations that render their dynamics truly stochastic. Here, we demonstrate that, despite this intrinsic randomness, the probability of occupying each phase state can be deterministically and continuously programmed. We experimentally investigate a vortex-based STNO operating under second-harmonic injection-locking, where the oscillator phase settles into two degenerate attractors separated by $\pi$ and undergoes thermally activated phase jumps. By applying a weak radio-frequency perturbation at the free-running frequency, we tune the phase-jump rates between the two attractors without suppressing the fluctuations, achieving continuous probability control from the unbiased limit to values approaching 0 or 1. The bias phase selects which attractor is favored while the bias amplitude sets the strength of the imbalance, providing two complementary control knobs within a single nanoscale device. A phase-reduced description based on an effective quasipotential quantitatively accounts for the observations. These results establish injection-locked STNOs as programmable stochastic elements and provide a hardware primitive for probabilistic computing, Ising machines, and brain-inspired computing architectures.

cond-mat.mes-hall↗

arXiv2026

Reconfigurable Multistate MRAM Synapses with Vortex STNO based Neurons for Scalable In-Memory Convolutional Neural Networks

Magnetic tunnel junction (MTJ)-based magnetic random-access memory (MRAM) is a promising platform for neuromorphic and in-memory computing owing to its non-volatility, high endurance, fast switching dynamics and CMOS compatibility. However, conventional spin-transfer torque and spin-orbit torque MRAM implementations for neural networks often suffer from high critical switching currents, large latency, thermal instability and significant read-write overheads. Here, we demonstrate a unified multistate MRAM-spin-torque nano-oscillator (STNO) architecture that integrates synapses and neurons on a single chip for convolutional neural network (CNN) applications. The system employs 1x8 multistate MRAM arrays as programmable synapses coupled with a vortex-based STNO neuron, enabling both individual and collective programming through fieldline-driven write channels. Multiple configurable resistance states are achieved by tuning internal and external magnetic fields together with bias currents, allowing quantized positive and negative synaptic weights for configurable kernel and pooling operations. The proposed architecture is evaluated through simulation on MNIST, SVHN, CIFAR-10, Google Speech Commands (GSC) and RadioML datasets, achieving accuracy of 99.76%, 87.93%, 78.14%, 87.96% and 56.46% respectively. Based on fabricated device dimensions, the complete architecture occupies ~6171.2 {\mu}m2 with an average energy consumption of 200.08 pJ per training and inference cycle for MNIST, highlighting its potential for scalable low-power neuromorphic computing

physics.app-ph↗

arXiv2026

Trainable Neuromorphic Spintronic Hardware Via Analog Finite-Difference Gradient Methods

Spintronic nano-neurons offer a promising route towards energy-efficient, high-performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However, training such networks remains a major bottleneck as it depends on oversimplified models of device behaviour and is highly sensitive to device variability. Here, we introduce a hardware architecture that overcomes these limitations by enabling on-device generation of gradients. First, we introduce theoretically and demonstrate experimentally that magnetic tunnel junctions can generate tunable and complex nonlinear responses. Building on this, we implement an analogue finite-difference approach to enable on-chip training in spintronic neural networks with one and two hidden layers. We experimentally implemented device in the loop backpropagation in a magnetic tunnel junction based neural network, achieving a classification accuracy of 93.3% despite pronounced device variability. During training, the gradients generated by the proposed analog neurons closely match the values derived numerically, without incurring computational overhead. Via physical simulations, we also demonstrate that this approach can be scaled up to support training in deep architectures. Our results pave the way for reliable, trainable and fully analogue spintronic neural networks, opening up new possibilities for next-generation, energy-efficient artificial intelligence hardware.

cond-mat.mes-hall↗

arXiv2025

Bio-integrated ${\mu}$Bots with Overtone Ultra-Wideband Magnetoelectric Antennas for Wireless Telemetry

Implantable and wearable devices require antennas that are both miniaturized and efficient, yet conventional designs are constrained by narrow bandwidth and orientation sensitivity. We report overtone ultra-wideband magnetoelectric (OUWB-ME) antennas that exploit higher order acoustic modes in polished silicon substrates to achieve a 22.6 GHz bandwidth in the 3-4 GHz range. Packaged into "${\mu}$Bots," these magnetoelectric heterostructures bonded with silver nanoparticle inks maintain stable operation under biological loading. In vitro assays confirm the biocompatibility of AlN and the protective role of parylene encapsulation for FeGa. Ex vivo rat and human tissues reshape transmission spectra, identifying reproducible frequency windows near 3.3 and 3.9 GHz. ${\mu}$Bots enable real-time audiovisual telemetry using software-defined radios and exhibit compatibility with 7T MRI. By combining wideband response, robustness to misalignment, and biocompatible packaging, OUWB-ME ${\mu}$Bots provide a scalable platform for wireless bio-integrated communication and telemetry.

physics.app-ph↗

arXiv2025

Experimental Study of a Vortex Spin-Torque Oscillator in an MTJ with a Vortex Polarizer

Spin-torque nano-oscillators (STNOs) are promising nanoscale microwave sources for spintronic applications, serving as signal generators or elements in neuromorphic computing systems. In this paper, we investigate the experimental realization of an oscillator based on a magnetic tunnel junction (MTJ) comprising two magnetic layers: a reference layer (RL) and a free layer (FL). We demonstrate that when magnetic vortices with opposite chirality and polarity are formed in the layers, the application of a current induces auto-oscillations even in the absence of external magnetic fields. This effect is observed in devices with diameters ranging from 800 to 1000 nm, exhibiting oscillation frequencies between 110 and 60 MHz. The underlying mechanism is attributed to the action of a spin current with vortex-like polarization injected from the RL, interacting with the magnetic vortex in the FL. This interaction generates a local out-of-plane effective field due to spin-transfer torque, which acts on the vortex core and initiates its motion. The observed mechanism differs qualitatively from the case of uniformly polarized spin currents perpendicular to the plane, where the resulting in-plane field acts on the planar components of the vortex magnetization.

cond-mat.other↗

arXiv2025

Reconfiguration of a Magnetic Tunnel Junction as a Way to Turn It into a Field-Free Vortex Oscillator

Magnetic tunnel junctions (MTJs) are key elements in practical spintronics, enabling not only conventional tasks such as data storage, transmission, and processing but also the implementation of compute-in-memory processing elements, facilitating the development of efficient hardware for neuromorphic computing. The functionality of an MTJ is determined by the properties of its free layer (FL) and reference layer (RL) with fixed magnetization, separated by an MgO tunnel barrier. This paper presents a mechanism for reconfiguring the RL, which is the upper layer of a pinned synthetic antiferromagnet, enabling a reversible transition from a single-domain state to a vortex magnetic state with different core positions. When the RL is in the vortex state, it generates a spin current with a vortex-like polarization distribution, enabling stable vortex oscillations in the FL even in the absence of external magnetic fields. This effect has been confirmed in MTJs with diameters ranging from 400 to 1000 nm. It is demonstrated, using experimental data with comparative micromagnetic simulation, that the pinning antiferromagnet retains a long term memory of previous reannealing states resulting in a deformation of the vortex polarised spin current, which in turn introduces a strong dynamical vortex core polarity symmetry breaking. The analogue reprogrammable nature of both the static and dynamic properties of the MTJ demonstrate different possible routes for the introduction of non-volatility into radiofrequency spintronic neuromorphic paradigms.

cond-mat.other↗

arXiv2025

Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing

Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures.

physics.optics↗

arXiv2024

Non-Volatile Analog Control and Reconfiguration of a Vortex Nano-Oscillator Frequency

Magnetic tunnel junctions are nanoscale devices which have recently attracted interested in the context of frequency multiplexed spintronic neural networks, due to their interesting dynamical properties, which are defined during the fabrication process, and depend on the material parameters and geometry. This paper proposes an approach to extending the functionality of a standard magnetic tunnel junction (MTJ) by introducing an additional ferromagnet/antiferromagnet (FM/AFM) storage layer (SL) vertically integrated with the standard Vortex MTJ stack into the nanopillar. The magnetostatic field created by this storage layer acts on the free layer and can be used to change its static and dynamic properties. To tune the magnitude and direction of this magnetostatic field, magnetic reconfiguration is carried out through a thermally assisted switching mechanism using a voltage pulse that heats the AFM layer in the SL above the Neel temperature in the presence of an external field. It is experimentally shown that using an MTJ based on a 600 nm diameter nanopillar with a vortex in the free layer, reconfiguration of the SL allows to continuously change the core precession frequency in the 15 MHz range. The reconfigurable analogue storage layer locally affects both the static and dynamic properties of the MTJ free layer, demonstrating vertical 3D integration of additional functionalities into a single MTJ nanopillar.

physics.app-ph↗

arXiv2023

Unbiased Random Number Generation using Injection-Locked Spin-Torque Nano-Oscillators

Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging nanoscale thermal magnetization fluctuations provides an efficient and almost cost-free means of generating truly random bitstreams, distinguishing them from predictable pseudo-random sequences. However, existing approaches that aim to achieve randomness often suffer from bias, leading to significant deviations from equal fractions of 0 and 1 in the bitstreams and compromising their inherent unpredictability. This study presents a hardware approach that capitalizes on the intrinsic balance of phase noise in an oscillator injection locked at twice its natural frequency, leveraging the stability of this naturally balanced physical system. We demonstrate the successful generation of unbiased and truly random bitstreams through extensive experimentation. Our numerical simulations exhibit excellent agreement with the experimental results, confirming the robustness and viability of our approach.

cond-mat.mtrl-sci↗

arXiv2023

The impact of local pinning sites in magnetic tunnel junctions with non-homogeneous free layers

Pinning at local defects is a significant road block for the successful implementation of technological paradigms which rely on the dynamic properties of non-trivial magnetic textures. In this report a comprehensive study of the influence of local pinning sites for non-homogeneous magnetic layers integrated as the free layer of a magnetic tunnel junction is presented, both experimentally and with corresponding micromagnetic simulations. The pinning sites are found to be extremely detrimental to the frequency controllability of the devices, a key requirement for their use as synapses in a frequency multiplexed artificial neural networks. In addition to describing the impact of the local pinning sites in the more conventional NiFe, a vortex-based magnetic tunnel junction with an amorphous free layer is presented which shows significantly improved frequency selectivity, marking a clear direction for the design of future low power devices.

cond-mat.mes-hall↗

arXiv2023

Enhancing Spin Transfer Torque in Magnetic Tunnel Junction Devices: Exploring the Influence of Capping Layer Materials and Thickness on Device Characteristics

We have developed and optimized two categories of spin transfer torque magnetic tunnel junctions (STT-MTJs) that exhibit a high tunnel magnetoresistance (TMR) ratio, low critical current, high outputpower in the micro watt range, and auto-oscillation behavior. These characteristics demonstrate the potential of STT-MTJs for low-power, high-speed, and reliable spintronic applications, including magnetic memory, logic, and signal processing. The only distinguishing factor between the two categories, denoted as A-MTJs and B-MTJs, is the composition of their free layers, 2 CoFeB/0.21 Ta/6 CoFeSiB for A-MTJs and 2 CoFeB/0.21 Ta/7 NiFe for B-MTJs. Our study reveals that B-MTJs exhibit lower critical currents for auto-oscillation than A-MTJs. We found that both stacks have comparable saturation magnetization and anisotropy field, suggesting that the difference in auto-oscillation behavior is due to the higher damping of A-MTJs compared to B-MTJs. To verify this hypothesis, we employed the all-optical time-resolved magneto-optical Kerr effect (TRMOKE) technique, which confirmed that STT-MTJs with lower damping exhibited auto-oscillation at lower critical current values. Additionally, our study aimed to optimize the STT-MTJ performance by investigating the impact of the capping layer on the device's response to electronic and optical stimuli.

physics.optics↗

arXiv2023

A semi-analytical model to simulate the spin-diode effect and accelerate its use in neuromorphic computing

The spin-diode effect is studied both experimentally and with our original semi-analytical method. The latter is based on an improved version of the Thiele equation approach (TEA) that we combine to micromagnetic simulation data to accurately model the non-linear dynamics of spin-torque vortex oscillator (STVO). This original method, called data-driven Thiele equation approach (DD-TEA), absorbs the difference between the analytical model and micromagnetic simulations to provide a both ultra-fast and quantitative model. The DD-TEA model predictions also agree very well with the experimental data. The reversal of the spin-diode effect with the chirality of the vortex, the impact of the input current and the origin of a variation at half of the STVO frequency are presented as well as the ability of the model to reproduce the experimental behavior. Finally, the spin-diode effect and its simulation using the DD-TEA model are discussed as a promising perspective in the framework of neuromorphic computing.

cond-mat.mes-hall↗

arXiv2023

Spin-Orbit Torque Flash Analog-to-Digital Converter

Although Analog-to-digital converters (ADCs) are critical components in mixed-signal integrated circuits (IC), their performance has not been improved significantly over the last decade. To achieve a radical improvement (compact, low power and reliable ADCs), spintronics can be considered as a proper candidate due to its compatibility with CMOS and wide applications in storage, neuromorphic computing, and so on. In this paper, a proof-of-concept of a 3-bit spin-CMOS Flash ADC using in-plane-anisotropy magnetic tunnel junctions (i-MTJs) with spin-orbit torque (SOT) switching mechanism is designed, fabricated and characterized. The proposed ADC replaces the current mirrors and power-hungry comparators in the conventional Flash ADC with seven parallel i-MTJs with different heavy metal (HM) widths. Monte-Carlo simulations based on the experimental measurements show the process variations/mismatch limits the accuracy of the proposed ADC to 2 bits. Moreover, the maximum differential nonlinearity (DNL) and integral nonlinearity (INL) are 0.739 LSB (least significant bit) and 0.7319 LSB, respectively.

cs.ET↗

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↗

arXiv2022

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↗