arXiv subjects

Hideo Ohno

Publications and source records attributed to Hideo Ohno.

At least 19 recordsLinked to original sources

arXiv2026

Single-shot laser-pulse-induced magnetization reversal in CoFeB/MgO-based magnetic tunnel junctions

We demonstrate single-shot laser-pulse-induced magnetization reversal in rare-earth-free CoFeB/MgO magnetic tunnel junctions (MTJs), a material system widely adopted in spin-transfer torque magnetic random-access memory (STT-MRAM). By tuning the Ru capping layer thickness, we modify the laser energy absorption profile and observe magnetization reversal from the parallel (P) to antiparallel (AP) state, with switching observed for $t_\text{Ru} \geq 2.0\,$ nm. Furthermore, we detect magnetization reversal in a micro-scale MTJ device via the tunnel magnetoresistance (TMR) effect. Our findings suggest that ultrafast spin transport, dipolar interactions, or a combination of both may contribute to the switching process, although the precise mechanism remains to be clarified. This work represents a significant step toward integrating ultrafast optical control with MTJ technology.

cond-mat.mes-hall↗

arXiv2026

Dynamical control of random telegraph noise in magnetic tunnel junctions

Faster random telegraph noise (RTN) in magnetic tunnel junctions (MTJs) would be beneficial for probabilistic computing applications. However, the interactions between the macrospin and spin waves with finite wave numbers reduce the RTN attempt frequency. We theoretically show that mode-selective heating and cooling by parametric excitation of Kittel mode or propagating spin waves can substantially increase or decrease the RTN frequency, respectively, and propose a nonlinear cooling mechanism that accelerates the switching dynamics. We outline experimental pathways to characterize the nonlinear processes that maximize the operation speed of MTJ-based probabilistic (p-) bits.

cond-mat.mes-hall↗

arXiv2026

CMOS-integrated superparamagnetic tunnel junction-based p-bit

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. A challenging threshold to cross for scaling sMTJ-based p-bit systems is integration of sMTJs with CMOS technology. In this work, we present experimental results of a p-bit unit cell using sMTJs integrated with 130 nm CMOS technology and demonstrate that the sMTJ's resistance fluctuations can generate a corresponding fluctuating digital output voltage which is tunable via the input voltage. These findings establish the feasibility of CMOS-compatible, sMTJ-based probabilistic circuits and mark a key step toward scalable hardware for real-world probabilistic computing applications.

cs.ET↗

arXiv2025

Size dependence of the properties of synthetic-antiferromagnet-based stochastic magnetic tunnel junctions for probabilistic computing

Stochastic magnetic tunnel junctions (s-MTJs) are core components for spintronics-based probabilistic computing (p-computing), a promising candidate for energy-efficient unconventional computing. To achieve reliable performance under practical conditions, the use of a synthetic antiferromagnetic (SAF) free-layer configuration was proposed due to its enhanced tolerance to magnetic field perturbations. For engineering the SAF s-MTJs, we systematically investigate the properties of the SAF s-MTJs as a function of the junction size. We observe that decreasing junction size leads to shorter relaxation times, enhanced magnetic field robustness, and enhanced insensitivity to bias voltage. These findings provide key insights toward high-performance p-computers with reliable operation.

physics.app-ph↗

arXiv2024

Nanoscale spin rectifiers for harvesting ambient radiofrequency energy

Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and thermodynamic limits and high-frequency parasitic impedance restrict the performance of state-of-the-art radiofrequency rectifiers. Nanoscale spin rectifiers based on magnetic tunnel junctions have recently demonstrated high sensitivity, but suffer from a low a.c.-to-d.c. conversion efficiency (less than 1%). Here, we report a sensitive spin rectifier rectenna that can harvest ambient radiofrequency signals between -62 and -20 dBm. We also develop an on-chip co-planar waveguide-based spin rectifier array with a large zero-bias sensitivity (around 34,500 mV/mW) and high efficiency (7.81%). Self-parametric excitation driven by voltage-controlled magnetic anisotropy is a key mechanism that contributes to the performance of the spin-rectifier array. We show that these spin rectifiers can wirelessly power a sensor at a radiofrequency power of -27 dBm.

cond-mat.mtrl-sci↗

arXiv2024

Effect of nonlinear magnon interactions on the stochastic magnetization switching

Telegraph noise caused by frequent switching of the magnetization in small magnetic devices has become a useful resource for probabilistic computing. Conventional theories have been based on a linearization of the fluctuations at the extrema of the magnetic free energy. We show theoretically that the non-linearities, specifically four-magnon scatterings, reduce the equilibrium fluctuation amplitude of the magnetization as well as the switching frequencies between local minima via the decay of the homogeneous Kittel mode into two spin waves with opposite momenta. Selectively suppressing the effective temperature of the finite-k spin waves, or reducing the radius of a thin magnetic disk enhance the switching frequency and improve performance of magnetic tunnel junctions in probabilistic computing applications.

cond-mat.mes-hall↗

arXiv2024

Voltage-insensitive stochastic magnetic tunnel junctions with double free layers

Stochastic magnetic tunnel junctions (s-MTJ) is a promising component of probabilistic bit (p-bit), which plays a pivotal role in probabilistic computers. For a standard cell structure of the p-bit, s-MTJ is desired to be insensitive to voltage across the junction over several hundred millivolts. In conventional s-MTJs with a reference layer having a fixed magnetization direction, however, the stochastic output significantly varies with the voltage due to spin-transfer torque (STT) acting on the stochastic free layer. In this work, we study a s-MTJ with a "double-free-layer" design theoretically proposed earlier, in which the fixed reference layer of the conventional structure is replaced by another stochastic free layer, effectively mitigating the influence of STT on the stochastic output. We show that the key device property characterized by the ratio of relaxation times between the high- and low-resistance states is one to two orders of magnitude less sensitive to bias voltage variations compared to conventional s-MTJs when the top and bottom free layers are designed to possess the same effective thickness. This work opens a pathway for reliable, nanosecond-operation, high-output, and scalable spintronics-based p-bits.

cond-mat.mes-hall↗

arXiv2024

Magnetic order in nanoscale gyroid networks

Three-dimensional magnetic metamaterials feature interesting phenomena that arise from a delicate interplay of material properties, local anisotropy, curvature, and connectivity. A particularly interesting magnetic lattice that combines these aspects is that of nanoscale gyroids, with a highly-interconnected chiral network with local three-connectivity reminiscent of three-dimensional artificial spin ices. Here, we use finite-element micromagnetic simulations to elucidate the anisotropic behaviour of nanoscale nickel gyroid networks at applied fields and at remanence. We simplify the description of the micromagnetic spin states with a macrospin model to explain the anistropic global response, to quantify the extent of ice-like correlations, and to discuss qualitative features of the anisotropic magnetoresistance in the three-dimensional network. Our results demonstrate the large variability of the magnetic order in extended gyroid networks, which might enable future spintronic functionalities, including neuromorphic computing and non-reciprocal transport.

cond-mat.mes-hall↗

arXiv2024

Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets

Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, sMTJs exhibiting the ideal characteristics necessary for probabilistic bits (p-bit) are still lacking. Ideally, the sMTJs should have (a) voltage bias independence preventing read disturbance (b) uniform randomness in the magnetization angle between the free layers, and (c) fast fluctuations without requiring external magnetic fields while being robust to magnetic field perturbations. Here, we propose a new design satisfying all of these requirements, using double-free-layer sMTJs with synthetic antiferromagnets (SAF). We evaluate the proposed sMTJ design with experimentally benchmarked spin-circuit models accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field surviving up to diameters exceeding ($D\approx 100$ nm) if the nanomagnets can be made thin enough ($\approx 1$-$2$ nm). The double-free-layer structure retains bias-independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full sMTJ model with advanced transistor models, we estimate the energy to generate a random bit as $\approx$ 3.6 fJ, with fluctuation rates of $\approx$ 3.3 GHz per p-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence.

cond-mat.mes-hall↗

arXiv2024

CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.

cond-mat.mes-hall↗

arXiv2023

Investigation of Spin-Wave Dynamics in Gyroid Nanostructures

A new concept in magnonics studies the dynamics of spin waves (SWs) in three-dimensional nanosystems. It is a natural evolution from conventionally used planar systems to explore magnetization configurations and dynamics in 3D nanostructures with lengths near intrinsic magnetic scales. In this work, we perform broadband ferromagnetic resonance (BBFMR) measurements and micromagnetic simulations of nanoscale magnetic gyroids - a periodic chiral structure consisting entirely of chiral triple junctions. Our results show unique properties of the network, such as the localization of the SW modes, evoking their topological properties, and the substantial sensitivity to the direction of the static magnetic field. The presented results open a wide range of applications in the emerging field of 3D magnonic crystals and spintronics.

cond-mat.mes-hall↗

arXiv2023

X-ray nanotomography reveals formation of single diamonds by block copolymer self-assembly

Block copolymers are recognised as a valuable platform for creating nanostructured materials with unique properties. Morphologies formed by block copolymer self-assembly can be transferred into a wide range of inorganic materials, enabling applications including energy storage and metamaterials. However, imaging of the underlying, often complex, nanostructures in large volumes has remained a challenge, limiting progress in materials development. Taking advantage of recent advances in X-ray nanotomography, we non-invasively imaged exceptionally large volumes of nanostructured soft materials at high resolution, revealing a single diamond morphology in a triblock terpolymer composite network. This morphology, which is ubiquitous in nature, has so far remained elusive in block copolymers, despite its potential to create materials with large photonic bandgaps. The discovery was made possible by the precise analysis of distortions in a large volume of the self-assembled diamond network, which are difficult to unambiguously assess using traditional characterisation tools. We anticipate that high-resolution X-ray nanotomography, which allows imaging of much larger sample volumes than electron-based tomography, will become a powerful tool for the quantitative analysis of complex nanostructures and that structures such as the triblock terpolymer-directed single diamond will enable the generation of advanced multicomponent composites with hitherto unknown property profiles.

cond-mat.soft↗

arXiv2023

High-resolution three-dimensional imaging of topological textures in single-diamond networks

Highly periodic structures are often said to convey the beauty of nature. However, most material properties are strongly influenced by the defects they contain. On the mesoscopic scale, molecular self-assembly exemplifies this interplay; thermodynamic principles determine short-range order, but long-range order is mainly impeded by the kinetic history of the material and by thermal fluctuations. For the development of self-assembly technologies, it is imperative to characterise and understand the interplay between self-assembled order and defect-induced disorder. Here we used synchrotron-based hard X-ray nanotomography to reveal a pair of extended topological defects within a self-assembled single-diamond network morphology. These defects are morphologically similar to the comet and trefoil patterns of equal and opposite half-integer topological charges observed in liquid crystals and appear to maintain a constant separation across the thickness of the sample, resembling pairs of full vortices in superconductors and other hard condensed matter systems. These results are expected to open new windows to study defect formation in soft condensed matter, particularly in biological systems where most structures are formed by self-assembly.

cond-mat.soft↗

arXiv2023

A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms

The transistor celebrated its 75${}^\text{th}$ birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

cs.ET↗

arXiv2022

Hardware-aware $in \ situ$ Boltzmann machine learning using stochastic magnetic tunnel junctions

One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that $in \ situ$ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.

cond-mat.mes-hall↗

arXiv2021

Unconventional Hall effect and its variation with Co-doping in van der Waals Fe3GeTe2

Two-dimensional (2D) van der Waals (vdW) magnetic materials have attracted a lot of attention owing to the stabilization of long-range magnetic order down to atomic dimensions, and the prospect of novel spintronic devices with unique functionalities. The clarification of the magnetoresistive properties and its correlation to the underlying magnetic configurations is essential for 2D vdW-based spintronic devices. Here, the effect of Co-doping on the magnetic and magnetotransport properties of Fe3GeTe2 have been investigated. Magnetotransport measurements reveal an unusual Hall effect behavior whose strength was considerably modified by Co-doping and attributed to arise from the underlying complicated spin textures. The present results provide a clue to tailoring of the underlying interactions necessary for the realization of a variety of unconventional spin textures for 2D vdW FM-based spintronics.

cond-mat.mtrl-sci↗

arXiv2021

Double Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits

Naturally random devices that exploit ambient thermal noise have recently attracted attention as hardware primitives for accelerating probabilistic computing applications. One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ) whose magnetic fluctuations are converted to resistance fluctuations in the presence of a stable fixed layer. Here, we propose and theoretically analyze a magnetic tunnel junction with no fixed layers but two free layers that are circularly shaped disk magnets. We use an experimentally benchmarked model that accounts for finite temperature magnetization dynamics, bias-dependent charge and spin-polarized currents as well as the dipolar coupling between the free layers. We obtain analytical results for statistical averages of fluctuations that are in good agreement with the numerical model. We find that the free layers with low diameters fluctuate to randomize the resistance of the MTJ in an approximately bias-independent manner. We show how such MTJs can be used to build a binary stochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs that need to operate at a specific bias point to produce random fluctuations, the proposed design can be random for a wide range of bias values, independent of spin-transfer-torque pinning. Moreover, in the absence of a carefully optimized stabled fixed layer, the symmetric double-free layer stack can be manufactured using present day Magnetoresistive Random Access Memory (MRAM) technology by minimal changes to the fabrication process. Such devices can be used as hardware accelerators in energy-efficient computing schemes that require a large throughput of tunably random bits.

cond-mat.mes-hall↗

arXiv2021

Generalized scaling of spin qubit coherence in over 12,000 host materials

Spin defect centers with long quantum coherence times ($T_2$) are key solid-state platforms for a variety of quantum applications. Recently, cluster correlation expansion (CCE) techniques have emerged as a powerful tool to simulate the $T_2$ of defect electron spins in these solid-state systems with good accuracy. Here, based on CCE, we uncover an algebraic expression for $T_2$ generalized for host compounds with dilute nuclear spin baths, which enables a quantitative and comprehensive materials exploration with a near instantaneous estimate of the coherence. We investigate more than 12,000 host compounds at natural isotopic abundance, and find that silicon carbide (SiC), a prominent widegap semiconductor for quantum applications, possesses the longest coherence times among widegap non-chalcogenides. In addition, more than 700 chalcogenides are shown to possess a longer $T_2$ than SiC. We suggest new potential host compounds with promisingly long $T_2$ up to 47 ms, and pave the way to explore unprecedented functional materials for quantum applications.

quant-ph↗