arXiv subjects

Mario Carpentieri

Publications and source records attributed to Mario Carpentieri.

At least 19 recordsLinked to original sources

arXiv2026

A magneto-mechanical gyroscope with spintronic readout

Gyroscopes are essential elements in navigation, consumer electronics, robotics, and aerospace applications. Most micro electro-mechanical systems (MEMS) implementations rely on capacitive sensing mechanisms, which limit the dimensional scaling to the micrometer scale. In this work, we introduce a MEMS-like two-degree-of-freedom (2-DOF) gyroscope that exploits the rectification functionality of magnetic tunnel junctions (MTJs) as its readout mechanism and as the transducer of the mechanical dynamics. Experimentally characterized MTJs have been used to calibrate and perform an experiment-informed design of the magneto-mechanical model combining micromagnetic theory with 2-DOF mechanical equations. We demonstrated that the output is linear with angular rate, and that the proposed device is able to extract the angular rate in dynamic cases exploiting a homodyne demodulation approach. The results open a path towards a compact, complementary metal-oxide semiconductor (CMOS)-compatible readout pathway that relaxes reliance on tight capacitive gaps and motivates multi-physics designs of the device.

cond-mat.mes-hall↗

arXiv2026

Fractional parametric resonance in spintronic diodes

Parametric pumping is a powerful tool for the excitation, amplification, and processing of oscillations and waves of different nature. In general, parametric resonance can occur when the pumping frequency $f_p$ and eigenfrequency of a linear mode (or wave) $f_0$ satisfy the relation $f_p$=2$f_0$/n (n=1,2,3,...). While such parametric resonance is well known in mechanical, superconductive, and quantum systems, in magnetic and spintronic systems only the lowest (n=1) parametric resonance at double the spin wave mode frequency $f_p$=2$f_0$ was thoroughly studied and explored. Here, using a theoretical analysis based on both micromagnetic simulations and an analytical model, we show the emergence of resonances at fractional frequencies $f_p$=2$f_0$/n (with n>10) in spintronic diodes driven by the simultaneous action of ac spin-transfer torque (STT, current densities < $10^6$ A/cm2) and voltage-controlled magnetic anisotropy (VCMA, effective anisotropy fields < 50 mT). The analytical model shows that parametric magnetization dynamics is irreducible to the standard Mathieu model of a parametric oscillator and demonstrates the crucial role of VCMA-driven mode frequency modulation: together with parametric coupling, it results in higher-order odd (n=3,5,7,...) fractional resonances, observed above certain VCMA pumping threshold, while simultaneous action with linear STT drive produces thresholdless even (n=4,6,8,...) resonances. This higher-order parametric dynamics is not restricted to VCMA pumping and opens new directions for the application of spintronic diodes for nonlinear signal processing and electromagnetic energy harvesting.

cond-mat.mes-hall↗

arXiv2026

A scalable and resource-efficient pipelined p-computer for probabilistic Ising machines

Probabilistic Ising machines (PIMs) based on probabilistic bits offer a hardware-friendly route to solve combinatorial optimization problems, but most digital implementations achieve high throughput by exploiting sparse interactions. This limits their applicability to dense problems, for which memory bandwidth and data movement become the dominant bottlenecks. Here, we show a resource-efficient pipelined Field-Programmable Gate Array architecture enabling high-throughput execution of fully-connected PIMs while maintaining scalability and modularity. This architecture design combines a deeply pipelined (>20 stages) probabilistic bit update path, which overlaps spin evaluation and local-field updates, with a bandwidth-aware on-chip memory organization for the coupling and bias matrices. The architecture supports 512 p-bits with 16-bit fixed-point coefficients and 1024 and 2048 p-bits with 10-bit and 2-bit coefficients, respectively, and operates at up to 300 MHz. At fixed degree of parallelization, it delivers an order-of-magnitude higher update rate than an optimized non-pipelined baseline, while improving the time-area trade-off for dense workloads. Validation on portfolio optimization and low-density parity-check decoding shows close agreement with software references and substantial reductions in time-to-solution relative to the non-pipelined design, establishing pipelining as an effective route to scalable digital probabilistic computing for dense optimization problems.

eess.SY↗

arXiv2026

Bias-Engineered Synthetic Antiferromagnets Hosting sub-20 nm Zero-Field Skyrmions at Room Temperature

Synthetic antiferromagnetic skyrmions (SAFsk) are nanoscale, topologically protected spin textures with strong potential for spintronic technologies because of their high stability and the absence of the skyrmion Hall effect. However, robust zero field stabilization remains a central challenge. Here, a synthetic antiferromagnetic (SAF) bias system is introduced as a novel strategy to stabilize both ferromagnetic skyrmions (FMsk) and SAFsk at zero field. Ferromagnetic (FM) and SAF multilayers are designed, fabricated and integrated with the SAF bias system to enable controlled skyrmion stabilization and polarity setting via multilayer design and a preparatory field cycle. Combining quantitative and high-sensitivity magnetic force microscopy (MFM) with micromagnetic modeling, reliable zero field skyrmion formation is demonstrated and sub 20nm SAFsk are directly observed, the smallest SAFsk reported to date. Moreover, the SAF bias system concept introduced here offers a robust and scalable route to bias future skyrmion multilayers, as its compensated nature suppresses domain formation and preserves a uniform exchange field.

cond-mat.mes-hall↗

arXiv2026

Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 antennas, outperforming or matching MMSE with reduced algorithmic complexity. Unlike the binary mapping, the p-dit interaction matrix is independent of the QAM order, enabling adaptive MIMO modulation. These results show a promising scalable paradigm for XL MIMO detection in future 6G networks.

cs.IT↗

arXiv2026

Beyond conventional skyrmions in synthetic antiferromagnets

Magnetic skyrmions are topologically protected spin textures that can act as reconfigurable nanoscale information carriers. In synthetic antiferromagnets (SAFs), interlayer exchange coupling offers an additional control parameter beyond the interfacial Dzyaloshinskii-Moriya interaction (DMI) and magnetic anisotropy. Here, we engineer a SAF composed of two chemically distinct ferromagnets (CoB and CoFeB), in which the external magnetic field and interlayer exchange act asymmetrically on the sublattices. The competition of these effects, acting as a resultant effective-field, gives rise to two distinct skyrmion families in different field regimes. In large fields, conventional-polarity skyrmions nucleate, with core antiparallel to the external field, whereas in smaller fields an inverse-polarity skyrmion state emerges as the effective-field reverses sign and almost saturates the CoFeB layers. Return-point memory measurements confirm independent nucleation pathways for the two families. Using element-resolved x-ray magnetometry, correlative magnetic force and Lorentz transmission electron microscopies, and parameter-matched micromagnetic modelling, we show that all textures reside only in the CoFeB layers, which experience a Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange field originating from the CoB layers. This effective-field method provides a robust route to programmable three-dimensional spin textures with controlled polarity in selected layers of a multilayer with potential for applications in skyrmion-based computing and spin-logic architectures.

cond-mat.mes-hall↗

arXiv2026

Laser-written reconfigurable energy landscapes and programmable Moir\'e spin textures

Magnetic textures are central to emerging spintronic and unconventional computing technologies due to their rich dynamics, topological properties and nanoscale dimensions. A major challenge remains achieving tunable, reversible, and spatially resolved control over these textures and their evolution as a function of external stimuli, by spatially reprogramming the magnetic energy landscape that governs their nucleation and stability. Here, we exploit a focused laser-assisted local field cooling technique that establishes a fast, non-contact and scalable platform for grayscale spin texture engineering. By non-destructively controlling the exchange-bias anisotropy with nanoscale resolution in thin-film heterostructures, this approach enables grayscale, reprogrammable control of the local energy profile, which we use to create magnetic patterns with highly controlled hysteresis, field-dependent readability and tunable switching thresholds. Leveraging this capability, we demonstrate information encoding with magnetic field-gated readability, and artificial spin metamaterials, stabilizing spin lattices with field-reconfigurable symmetries and creating artificial Moir\'e spin textures via the geometric superposition of twisted magnetic potentials. These results establish a versatile, reprogrammable platform that bridges the gap between application-oriented magnetic memory and fundamental studies of emergent order in artificial lattices.

cond-mat.mtrl-sci↗

arXiv2026

A CMOS-compatible, scalable and compact magnetoelectric spin-torque microwave detector

The development of compact and highly sensitive microwave detectors compatible with complementary-metal-oxide-semiconductor (CMOS) processes is an active research area but remains a major challenge in microwave technology. Spin-torque diodes (STDs) are emerging nanoscale spintronic devices capable of surpassing the theoretical thermodynamic sensitivity limits of Schottky diodes. However, their practical use in compact systems is limited by the need of external antennas or probes. Here, we demonstrate a magnetoelectric (ME) spin-torque microwave detector that monolithically integrates an ME antenna with a magnetic tunnel junction (MTJ). The device directly converts wireless electromagnetic signals into a DC output at sub-microwatt power levels, achieving a sensitivity greater than 90 kV/W, a noise equivalent power of 3 pW*Hz^-0.5, and a compact footprint of 0.4 mm^2. This performance is due to the nonlinear coupling between incoherent magnetization dynamics, driven by a DC current in the MTJ, and the combined effects of the microwave voltage and strain generated by the ME antenna under incident electromagnetic waves. We further show that this design is scalable, enabling the co-integration of an ME antenna with an array of MTJs. A detector incorporating four MTJs, for example, exhibits a sensitivity exceeding 400 kV/W. This work paves the way for a new generation of highly sensitive, compact and scalable microwave detectors that combine ME antennas and spintronic diodes.

cond-mat.mes-hall↗

arXiv2026

Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning

Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estimating the interfacial Dzyaloshinskii-Moriya interaction strength remains challenging, as existing experimental methods often rely on indirect measurements and can yield inconsistent results across techniques. Because this interaction is often extracted experimentally from bubble domain expansion, we investigate whether bubble textures alone contain sufficient and reliable information for data driven DMI inference. We therefore develop a compact convolutional neural network trained on a comprehensive micromagnetic dataset of magnetic bubble domains designed to emulate magneto optical Kerr effect imaging, including structural non uniformity, additive noise, and image pixelation. The proposed network demonstrates strong robustness against sample inhomogeneities, noise, and reduced spatial resolution. Furthermore, it exhibits reliable generalization by accurately predicting DMI values outside the trained interval. These results support the use of machine learning as a fast and quantitative tool to characterize magnetic textures with interfacial DMI.

cond-mat.mtrl-sci↗

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

Adaptive Ising machine based on phase-locking of an auto-oscillator to a bi-harmonic external driving with noise

We introduce a universal theory of phase auto-oscillators driven by a bi harmonic signal (having frequency components close to single and double of the free-running oscillator frequency) with noise. With it, we show how deterministic phase locking and stochastic phase slips can be continuously tuned by varying the relative amplitudes and frequencies of the driving components. Using, as an example, a spin-torque nano-oscillator, we numerically validate this theory by implementing a deterministic Ising machine paradigm, a probabilistic one, and dual-mode operation of the two. This demonstration introduces the concept of adaptive Ising machines (AIM), a unified oscillator-based architecture that dynamically combines both regimes within the same hardware platform by properly tuning the amplitudes of the bi-harmonic driving relative to the noise strength. Benchmarking on different classes of combinatorial optimization problems, the AIM exhibits complementary performance compared to oscillator based Ising machines and probabilistic Ising machines, with adaptability to the specific problem class. This work introduces the first OIM capable of transitioning between deterministic and probabilistic computation taking advantage of a proper design of the trade-off between the strength of phase-locking of an auto-oscillator to a bi harmonic external driving and noise, opening a path toward scalable, CMOS compatible hardware for hybrid optimization and inference.

cond-mat.mes-hall↗

arXiv2025

An all-magnonic neuron with tunable fading memory

Magnonics offers nanometer-scale wave propagation and strong nonlinearities, making it attractive for neuromorphic applications such as artificial neurons. Yet, magnonic elements with interconnections solely within the magnonic system remain challenging, preventing the realization of interconnected magnonic neurons to date. Here, we experimentally demonstrate an all-magnonic neuron that reacts to magnon inputs with thresholded, amplified magnon firing and subsequent self-reset, enabling all-magnonic operation and cascading. Our approach is based on micro-antenna excitation on an ultra-low damping garnet with perpendicular magnetic anisotropy (PMA), where we exploit the positive magnon frequency shift to realize nonlinear activation. Using Brillouin light scattering spectroscopy, we uncover a transient neuron response with tunable fading memory: A 25% change in pump power results in a 3-order-of-magnitude tuning in memory time, which we harness, demonstrating temporal integration of up to 50 magnon pulses. Finally, we realize neuron triggering in a cascade of 3 neurons, highlighting its potential for connected magnonic circuits.

cond-mat.mtrl-sci↗

arXiv2025

Octupole-driven spin torque switching of antiferromagnetic tunnel junctions

Magnetic tunnel junctions (MTJs) based on ferromagnets are canonical devices in spintronics, with wide-ranging applications in data storage, computing, and sensing. They simultaneously exhibit mechanisms for electrical detection and control of magnetic order through the tunneling magnetoresistance (TMR) and spin-transfer torque (STT) effects, respectively. It was long assumed that neither of these effects could be sizeable in all-antiferromagnetic tunnel junctions (AATJs), since they exhibit no net magnetization. Recently, however, it was shown that AATJs based on chiral antiferromagnets do exhibit TMR due to their non-relativistic momentum-dependent spin polarization and cluster magnetic octupole moment (CMO), which are manifestations of their spin-split band structure. However, the reciprocal effect, i.e., the antiferromagnetic counterpart of STT, has been assumed non-existent due to the total electric current being spin-neutral. Here, we report nanoscale AATJs exhibiting this reciprocal effect, which we term octupole-driven spin-transfer torque (OTT). We demonstrate current-induced OTT switching of PtMn3|MgO|PtMn3 AATJs, exhibiting a TMR value of 363% at room temperature and switching current densities of the order of 10 MA/cm2. Our theoretical modeling explains the origin of OTT in terms of the imbalance between intra- and inter-sublattice spin currents across the AATJ, and equivalently, in terms of the non-zero net cluster octupole polarization of each PtMn3 layer. This work establishes a new materials platform for antiferromagnetic spintronics and provides a pathway towards deeply scaled magnetic memory and room-temperature terahertz technologies.

cond-mat.mtrl-sci↗

arXiv2025

Multi-value Probabilistic Computing with current-controlled Skyrmion Diffusion

Magnetic systems are highly promising for implementing probabilistic computing paradigms because of the fitting energy scales and conspicuous non-linearities. While conventional binary probabilistic computing has been realized, implementing more advantageous multi-value probabilistic computing (MPC) remains a challenge. Here, we report the realization of MPC by leveraging the thermally activated diffusion of magnetic skyrmions through an effectively non-flat energy landscape defined by a discrete number of pinning sites. The time-averaged spatial distribution of the diffusing skyrmions directly realizes a discrete probability distribution, which is tunable by current-generated spin-orbit torques, and can be quantified by non-perturbative electrical measurements. Even a very straightforward implementation with global tuning, already allows us to demonstrate the softmax computation - a core function in artificial intelligence. As a key advance, we demonstrate invertible logic without the need to create a network of probabilistic devices, offering major scalability advantages. Our proof of concept can be generalized to multiple skyrmions and can accommodate multiple locally tunable inputs and outputs using magnetic tunnel junctions, potentially enabling the representation of highly complex distribution functions.

cond-mat.mtrl-sci↗

arXiv2025

Spintronic temperature nanosensor based on the resonance response of a skyrmion-hosting magnetic tunnel junction

The increasing need for efficient thermal management in nanoelectronics requires innovative thermal sensing solutions, as conventional sensors often exhibit nonlinear responses, low sensitivity, and complex calibration. We predict a temperature dependence in the response of existing skyrmion based spintronic diodes and propose their use as nanoscale thermal sensors. These devices leverage magnetic skyrmions topologically protected spin textures known for their robustness, nanoscale dimensions, and low power dynamics. We demonstrate high thermal sensitivity with a linear temperature response over a wide range. This linearity, observed in both the amplitude and frequency of the skyrmion excitation, ensures redundancy that enables precise and reliable temperature measurement. In addition, the use of multilayer systems enhances the sensitivity and robustness of the device. These results provide a foundation for skyrmion-based caloritronic devices with promising applications in spintronic sensors, thermal management, nanoelectronics, and skyrmion-caloritronics.

cond-mat.mes-hall↗

arXiv2025

Spin-Torque-Driven Non-uniform Dynamics of an Antivortex Core in Truncated Astroid Shaped Nanomagnets

Spin textures that are not readily available in the domain structures of continuous magnetic thin films can be stabilized when patterned to micro/nano scales due to the dominant effect of dipolar magnetic interactions. Fabrication of such devices enables a thorough study of their RF dynamics excited by highly concentrated spin-polarized/pure-spin currents. For this purpose, in this study, we have employed a truncated astroid geometry to achieve stable magnetic antivortex core nucleation/annihilation which was detectable using the anisotropic magnetoresistance (AMR) at various temperatures. Furthermore, by depositing a soft magnetic thin film (20 nm thick permalloy) capped with a heavy-metal 2nm Pt layer, we were able to probe the spin orbit torque induced excitations accompanied by self-torque due to half-antivortex cores reminiscent of an isolated-antivortex, yielding GHz frequency oscillations with high quality factors (~50000). The observed RF oscillations can be attributed to a non-uniform domain wall oscillation mode close to the stable-antivortex core nucleation site as seen in micromagnetic simulations. This fundamental study of antivortex core response to spin currents is crucial for the assessment of their potential applications in high frequency spintronic devices such as reservoir computers.

cond-mat.mes-hall↗

arXiv2025

High-performance and reliable probabilistic Ising machine based on simulated quantum annealing

Probabilistic computing with pbits is emerging as a computational paradigm for machine learning and for facing combinatorial optimization problems (COPs) with the so-called probabilistic Ising machines (PIMs). From a hardware point of view, the key elements that characterize a PIM are the random number generation, the nonlinearity, the network of coupled pbits, and the energy minimization algorithm. Regarding the latter, in this work we show that PIMs using the simulated quantum annealing (SQA) schedule exhibit better performance as compared to simulated annealing and parallel tempering in solving a number of COPs, such as maximum satisfiability problems, planted Ising problem, and travelling salesman problem. Additionally, we design and simulate the architecture of a fully connected CMOS based PIM able to run the SQA algorithm having a spin-update time of 8 ns with a power consumption of 0.22 mW. Our results also show that SQA increases the reliability and the scalability of PIMs by compensating for device variability at an algorithmic level enabling the development of their implementation combining CMOS with different technologies such as spintronics. This work shows that the characteristics of the SQA are hardware agnostic and can be applied in the co-design of any hybrid analog digital Ising machine implementation. Our results open a promising direction for the implementation of a new generation of reliable and scalable PIMs.

cond-mat.mes-hall↗

arXiv2025

Spin wave eigenmodes in nanoscale magnetic tunnel junctions with perpendicular magnetic anisotropy

Magnetic tunnel junctions (MTJs) are key enablers of spintronic technologies used in a variety of applications including information storage, microwave generation and detection, as well as unconventional computing. Here, we present experimental and theoretical studies of quantized spin wave eigenmodes in perpendicular MTJs focusing on a coupled magnetization dynamics in the free (FL) and reference (RL) layers of the MTJ, where the RL is a synthetic antiferromagnet (SAF). Spin-torque ferromagnetic resonance (ST-FMR) measurements reveal excitation of two spin wave eigenmodes in response to applied microwave current. These modes show opposite frequency shifts as a function of out-of-plane magnetic field. Our micromagnetic simulations accurately reproduce the dependence of the mode frequencies on magnetic field and reveal the spatial profiles of the excitations in the FL and RL. The FL and RL modes generate rectified voltage signals of opposite polarity, which makes this device a promising candidate for a tunable dual-frequency microwave signal detector. The simulations show that weak interlayer exchange coupling within the SAF enhances the mode amplitudes. We also calculate the response of the detector as a function of in-plane magnetic field bias and find that its sensitivity significantly increases with increasing field. We experimentally confirm this prediction via ST-FMR measurements as a function of in-plane magnetic field. Our results provide deeper understanding of quantized spin wave eigenmodes in nanoscale MTJs with perpendicular magnetic anisotropy and demonstrate the potential of these devices for frequency-selective dual-channel microwave signal detectors.

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