arXiv subjects

Shunsuke Fukami

Publications and source records attributed to Shunsuke Fukami.

At least 37 records · Page 2Linked to original sources

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

Anisotropic magnetotransport in the layered antiferromagnet TaFe$_{1.25}$Te$_3$

The discovery of fascinating ways to control and manipulate antiferromagnetic materials have garnered considerable attention as an attractive platform to explore novel spintronic phenomena and functionalities. Layered antiferromagnets (AFMs) exhibiting interesting magnetic structures, can serve as an attractive starting point to establish novel functionalities down to the two-dimensional limit. In this work, we explore the magnetoresistive properties of the spin-ladder AFM TaFe$_{1.25}$Te$_3$. Magnetization studies reveal an anisotropic magnetic behavior resulting in the stabilization of a spin-flop configuration for H $\perp$ (10-1) plane (i.e., out-of-plane direction). Angle-dependent longitudinal and transverse magnetoresistances show an unusual anharmonic behavior. A significant anisotropic enhancement of magnetoresistance when H $\perp$ (10-1) plane compared to H $\parallel$ (10-1) directions has been observed. The present results deepen our understanding of the magnetoresistive properties of low-dimensional layered AFMs, and point towards the possibility of utilizing these novel material systems for antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

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

Modification of unconventional Hall effect with doping at the non-magnetic site in a 2D van der Waals ferromagnet

Two-dimensional (2D) van der Waals (vdW) magnetic materials have garnered considerable attention owing to the existence of magnetic order down to atomic dimensions and flexibility towards interface engineering, offering an attractive platform to explore novel spintronic phenomena and functionalities. Understanding of the magnetoresistive properties and their correlation to the underlying magnetic configurations is essential for 2D vdW-based spintronic or quantum information devices. Among the promising candidates, vdW ferromagnet (FM) Fe3GeTe2 shows an unusual magnetotransport behavior, tunable by doping at the magnetic (Fe) site, and tentatively arising from complicated underlying spin texture configurations. Here, we explore an alternative route towards manipulation of magnetotransport properties of a vdW FM without directly affecting the magnetic site i.e., by doping at the non-magnetic (Ge) site of Fe3(Ge,As)Te2. Interestingly, doping at the non-magnetic (Ge) site results in an unconventional Hall effect whose strength was considerably modified by increasing As concentration, possibly arising from emergent electromagnetic behavior from underlying complicated spin configurations. The present results provide a possible route to understand the intricate role played by the non-magnetic (Ge) atom towards magnetic properties of vdW FMs, and shows a novel direction towards tailoring of underlying interactions responsible for the stabilization of non trivial spin textures in 2D magnetic vdW materials.

cond-mat.str-el↗

arXiv2021

Large Exotic Spin Torques in Antiferromagnetic Iron Rhodium

Spin torque is a promising tool for driving magnetization dynamics for novel computing technologies. These torques can be easily produced by spin-orbit effects, but for most conventional spin source materials, a high degree of crystal symmetry limits the geometry of the spin torques produced. Magnetic ordering is one way to reduce the symmetry of a material and allow exotic torques, and antiferromagnets are particularly promising because they are robust against external fields. We present spin torque ferromagnetic resonance measurements and second harmonic Hall measurements characterizing the spin torques in antiferromagnetic iron rhodium alloy. We report extremely large, strongly temperature-dependent exotic spin torques with a geometry apparently defined by the magnetic ordering direction. We find the spin torque efficiency of iron rhodium to be (330$\pm$150) % at 170 K and (91$\pm$32) % at room temperature. We support our conclusions with theoretical calculations showing how the antiferromagnetic ordering in iron rhodium gives rise to such exotic torques.

cond-mat.mtrl-sci↗

arXiv2021

Emergent inductance by dynamical Aharonov-Casher phases

We propose a mechanism of inductance operation originating from a dynamical Aharonov-Casher (AC) phase of an electron in ferromagnets. By taking into account spin-orbit coupling effects, we extend the theory of emergent inductance, which has recently been discovered in spiral magnets, to arbitrary magnetic textures. The inductance of dynamical AC phase origin universally arises in the coexistence of magnetism and a spin-orbit coupling, even with spatially-uniform magnetization, allowing its stable operation in wide ranges of temperature and frequency. Revisiting the widely studied systems, such as ferromagnets with spatial inversion asymmetry, with the new perspective offered by our work will lead to opening a new paradigm in the study of AC phase physics and the spintronics-based power management in ultra-wideband frequency range.

cond-mat.mes-hall↗

arXiv2021

Roadmap of spin-orbit torques

Spin-orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin-orbit coupling, conduction electron spins and magnetization. More recently interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this Roadmap paper, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, two-dimensional materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three-terminal and two-terminal SOT-magnetoresistive random-access memories (MRAMs); we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain wall and skyrmion racetrack memories. This paper aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors.

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↗

arXiv2020

Memristive control of mutual SHNO synchronization for neuromorphic computing

Synchronization of large spin Hall nano-oscillators (SHNO) arrays is an appealing approach toward ultra-fast non-conventional computing based on nanoscale coupled oscillator networks. However, for large arrays, interfacing to the network, tuning its individual oscillators, their coupling, and providing built-in memory units for training purposes, remain substantial challenges. Here, we address all these challenges using memristive gating of W/CoFeB/MgO/AlOx based SHNOs. In its high resistance state (HRS), the memristor modulates the perpendicular magnetic anisotropy (PMA) at the CoFeB/MgO interface purely by the applied electric field. In its low resistance state (LRS), and depending on the voltage polarity, the memristor adds/subtracts current to/from the SHNO drive. The operation in both the HRS and LRS affects the SHNO auto-oscillation mode and frequency, which can be tuned up to 28 MHz/V. This tuning allows us to reversibly turn on/off mutual synchronization in chains of four SHNOs. We also demonstrate two individually controlled memristors to tailor both the coupling strength and the frequency of the synchronized state. Memristor gating is therefore an efficient approach to input, tune, and store the state of the SHNO array for any non-conventional computing paradigm, all in one platform.

physics.app-ph↗

arXiv2020

Current distribution in metallic multilayers from resistance measurements

The in-plane current profile within multilayers of generic structure Ta/Pt/(CoNi)/Pt/Ta is investigated. A large set of samples where the thickness of each layer is systematically varied was grown, followed by the measurement of the sheet resistance of each sample. The data are analyzed by a series of increasingly elaborate models, from a macroscopic engineering approach to mesoscopic transport theory. Non-negligible variations of the estimated repartition of current between the layers are found. The importance of having additional structural data is highlighted.

cond-mat.mtrl-sci↗

arXiv2020

Giant voltage control of spin Hall nano-oscillator damping

Spin Hall nano-oscillators (SHNOs) are emerging spintronic devices for microwave signal generation and oscillator based neuromorphic computing combining nano-scale footprint, fast and ultra-wide microwave frequency tunability, CMOS compatibility, and strong non-linear properties providing robust large-scale mutual synchronization in chains and two-dimensional arrays. While SHNOs can be tuned via magnetic fields and the drive current, neither approach is conducive for individual SHNO control in large arrays. Here, we demonstrate electrically gated W/CoFeB/MgO nano-constrictions in which the voltage-dependent perpendicular magnetic anisotropy, tunes the frequency and, thanks to nano-constriction geometry, drastically modifies the spin-wave localization in the constriction region resulting in a giant 42 % variation of the effective damping over four volts. As a consequence, the SHNO threshold current can be strongly tuned. Our demonstration adds key functionality to nano-constriction SHNOs and paves the way for energy-efficient control of individual oscillators in SHNO chains and arrays for neuromorphic computing.

physics.app-ph↗