Kagomerization of transition metal monolayers induced by two-dimensional hexagonal boron nitride.

The kagome lattice is an exciting solid state physics platform for the emergence of nontrivial quantum states driven by electronic correlations: topological effects, unconventional superconductivity, charge and spin density waves, and unusual magnetic states such as quantum spin liquids. While kagome lattices have been realized in complex multi-atomic bulk compounds, here we demonstrate from first-principles a process that we dub kagomerization, in which we fabricate a two-dimensional kagome lattice in monolayers of transition metals utilizing an hexagonal boron nitride (h-BN) overlayer. Surprisingly, h-BN induces a large rearrangement of the transition metal atoms supported on a fcc(111) heavy-metal surface. This reconstruction is found to be rather generic for this type of heterostructures and has a profound impact on the underlying magnetic properties, ultimately stabilizing various topological magnetic solitons such as skyrmions and bimerons. Our findings call for a reconsideration of h-BN as merely a passive capping layer, showing its potential for not only reconstructing the atomic structure of the underlying material, e.g. through the kagomerization of magnetic films, but also enabling electronic and magnetic phases that are highly sought for the next generation of device technologies.

An Energy-Efficient Bayesian Neural Network Implementation Using Stochastic Computing Method.

The robustness of Bayesian neural networks (BNNs) to real-world uncertainties and incompleteness has led to their application in some safety-critical fields. However, evaluating uncertainty during BNN inference requires repeated sampling and feed-forward computing, making them challenging to deploy in low-power or embedded devices. This article proposes the use of stochastic computing (SC) to optimize the hardware performance of BNN inference in terms of energy consumption and hardware utilization. The proposed approach adopts bitstream to represent Gaussian random number and applies it in the inference phase. This allows for the omission of complex transformation computations in the central limit theorem-based Gaussian random number generating (CLT-based GRNG) method and the simplification of multipliers as and operations. Furthermore, an asynchronous parallel pipeline calculation technique is proposed in computing block to enhance operation speed. Compared with conventional binary radix-based BNN, SC-based BNN (StocBNN) realized by FPGA with 128-bit bitstream consumes much less energy consumption and hardware resources with less than 0.1% accuracy decrease when dealing with MNIST/Fashion-MNIST datasets.

Anomalous Thermal-Assisted Spin-Orbit Torque-Induced Magnetization Switching for Energy-Efficient Logic-in-Memory.

Spin-orbit torque (SOT) is widely considered as an effective route to manipulate magnetic order in spintronic devices. The low power consumption and long endurance demands from future computer architectures urgently require a reduction of the critical SOT switching current density, jsw. However, except for searching for a SOT source with a high-spin Hall angle, few efficient mechanisms to reduce jsw have been proposed. In this work, we achieved an anomalous thermal-assisted (TA) jsw reduction in a Pt/Co/Tb heterostructure through engineering a ferrimagnetic Co/Tb interface. This jsw reduction tendency is demonstrated to be strongly dependent on the thickness of Tb, tTb. When tTb reaches an optimal point (3 nm), a 74 K temperature increase will reduce jsw by more than an order of magnitude (17 times). Comparison experiments and theoretical simulations indicate that this anomalous TA reduction behavior goes beyond the conventional SOT framework and originates from the temperature-sensitive ferrimagnetic interface. We further propose a multifunctional logic-in-memory device, where six different Boolean logic gates can be implemented, to demonstrate the application potential and energy efficiency of this TA SOT switching mechanism. Our work provides an effective alternative to reduce jsw in SOT devices and may inspire future spintronic memory, logic, and high-frequency devices.

High On/Off Ratio Spintronic Multi-Level Memory Unit for Deep Neural Network.

Spintronic devices are considered as one of the most promising technologies for non-volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi-level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi-level memory unit with high on/off ratio is proposed by integrating several series-connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing-in-memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high-accuracy and robust artificial intelligence applications.

Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with a vertical composition gradient.

Current-induced spin-orbit torques (SOTs) are of interest for fast and energy-efficient manipulation of magnetic order in spintronic devices. To be deterministic, however, switching of perpendicularly magnetized materials by SOT requires a mechanism for in-plane symmetry breaking. Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications. Here, we report bias-field-free SOT switching in a single perpendicular CoTb layer with an engineered vertical composition gradient. The vertical structural inversion asymmetry induces strong intrinsic SOTs and a gradient-driven Dzyaloshinskii-Moriya interaction (g-DMI), which breaks the in-plane symmetry during the switching process. Micromagnetic simulations are in agreement with experimental results, and elucidate the role of g-DMI in the deterministic switching processes. This bias-field-free switching scheme for perpendicular ferrimagnets with g-DMI provides a strategy for efficient and compact SOT device design.

Tunable Tunneling Magnetoresistance in van der Waals Magnetic Tunnel Junctions with 1T-CrTe2 Electrodes.

Two-dimensional (2D) van der Waals (vdW) heterostructures have opened new avenues for spintronic applications with novel properties. Here, by density functional theory calculations, we investigated the spin-dependent transport in vdW magnetic tunnel junctions (MTJs) composed of 1T-CrTe2 ferromagnetic electrodes. Meanwhile, graphene and h-BN are employed as tunnel barriers. It has been found that the tunneling magnetoresistance (TMR) effects of two types of vdW MTJs present analogous trends: thicknesses of barriers have a great influence on the TMR ratios, which reach up to the maximum when barriers increase to five monolayers. However, despite the similarity, the graphene-barrier junction is more promising for optimization. Through observing the energy-resolved transmission spectra of vdW MTJs, we noticed that TMR ratios of graphene-barrier junctions are tunable and could be enhanced through tuning the position of Fermi energy. Therefore, we successfully realized the TMR optimization by substitutional doping. When substituting one carbon atom with one boron atom in the graphene barrier, TMR ratios are drastically improved, and a TMR ratio as high as 6962% could be obtained in the doped seven-monolayer-barrier junction. Our results pave the way for vdW MTJ applications in spintronics.

Robust phase shift keying modulation method for spin torque nano-oscillator.

The spin torque nano-oscillator (STNO) is a very promising candidate for next generation telecommunication systems due to its small size ~100 nm and high output frequency range. However, it still suffers low output power, usually smaller than µW, and very high phase noise. Also, the modulation method for the STNO should be further developed. The frequency modulation and amplitude modulation method for STNO can be easily applied because of the non-linear nature of STNO, yet it is very rare to see the proposal of a phase modulation method. In this work, we propose a robust phase shift keying modulation method for STNO. Its feasibility is demonstrated with both theoretical and numerical analysis, and its robustness is investigated under room temperature thermal noise. It is shown that our proposed phase modulation method can tune the phase arbitrarily, while the modulation speed can be as fast as 10 ns at room temperature. Comparing with the other phase modulation method, our approach has advantages of larger phase tuning range and stronger robustness against thermal noise.

Author Correction: Record thermopower found in an IrMn-based spintronic stack.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Record thermopower found in an IrMn-based spintronic stack.

The Seebeck effect converts thermal gradients into electricity. As an approach to power technologies in the current Internet-of-Things era, on-chip energy harvesting is highly attractive, and to be effective, demands thin film materials with large Seebeck coefficients. In spintronics, the antiferromagnetic metal IrMn has been used as the pinning layer in magnetic tunnel junctions that form building blocks for magnetic random access memories and magnetic sensors. Spin pumping experiments revealed that IrMn Néel temperature is thickness-dependent and approaches room temperature when the layer is thin. Here, we report that the Seebeck coefficient is maximum at the Néel temperature of IrMn of 0.6 to 4.0 nm in thickness in IrMn-based half magnetic tunnel junctions. We obtain a record Seebeck coefficient 390 (±10) µV K-1 at room temperature. Our results demonstrate that IrMn-based magnetic devices could harvest the heat dissipation for magnetic sensors, thus contributing to the Power-of-Things paradigm.

A compact skyrmionic leaky-integrate-fire spiking neuron device.

Neuromorphic computing, which relies on a combination of a large number of neurons massively interconnected by an even larger number of synapses, has been actively studied for its characteristics such as energy efficiency, intelligence, and adaptability. To date, while the development of artificial synapses has shown great progress with the introduction of emerging nanoelectronic devices, e.g., memristive devices, the implementation of artificial neurons, however, depends mostly on semiconductor-based circuits via integrating many transistors, sacrificing energy efficiency and integration density. Here, we present a novel compact neuron device that exploits the current-driven magnetic skyrmion dynamics in a wedge-shaped nanotrack. Under the coaction of the exciting current pulse and the repulsive force exerted by the nanotrack edges, the dynamic behavior of the proposed skyrmionic artificial neuron device is in analogy to the leaky-integrate-fire (LIF) spiking function of a biological neuron. The tunable temporary location of the skyrmion in our artificial neuron behaves like the analog membrane potential of a biological neuron. The neuronal dynamics and the related physical interpretations of the proposed skyrmionic neuron device are carefully investigated via micromagnetic and theoretical methods. Such a compact artificial neuron enables energy-efficient and high-density implementation of neuromorphic computing hardware.

Long-distance propagation of short-wavelength spin waves.

Recent years have witnessed a rapidly growing interest in exploring the use of spin waves for information transmission and computation toward establishing a spin-wave-based technology that is not only significantly more energy efficient than the CMOS technology, but may also cause a major departure from the von-Neumann architecture by enabling memory-in-logic and logic-in-memory architectures. A major bottleneck of advancing this technology is the excitation of spin waves with short wavelengths, which is a must because the wavelength dictates device scalability. Here, we report the discovery of an approach for the excitation of nm-wavelength spin waves. The demonstration uses ferromagnetic nanowires grown on a 20-nm-thick Y3Fe5O12 film strip. The propagation of spin waves with a wavelength down to 50 nm over a distance of 60,000 nm is measured. The measurements yield a spin-wave group velocity as high as 2600 m s-1, which is faster than both domain wall and skyrmion motions.

Current-induced magnetization switching in atom-thick tungsten engineered perpendicular magnetic tunnel junctions with large tunnel magnetoresistance.

Perpendicular magnetic tunnel junctions based on MgO/CoFeB structures are of particular interest for magnetic random-access memories because of their excellent thermal stability, scaling potential, and power dissipation. However, the major challenge of current-induced switching in the nanopillars with both a large tunnel magnetoresistance ratio and a low junction resistance is still to be met. Here, we report spin transfer torque switching in nano-scale perpendicular magnetic tunnel junctions with a magnetoresistance ratio up to 249% and a resistance area product as low as 7.0 Ω µm2, which consists of atom-thick W layers and double MgO/CoFeB interfaces. The efficient resonant tunnelling transmission induced by the atom-thick W layers could contribute to the larger magnetoresistance ratio than conventional structures with Ta layers, in addition to the robustness of W layers against high-temperature diffusion during annealing. The critical switching current density could be lower than 3.0 MA cm-2 for devices with a 45-nm radius.

Ring-shaped Racetrack memory based on spin orbit torque driven chiral domain wall motions.

Racetrack memory (RM) has sparked enormous interest thanks to its outstanding potential for low-power, high-density and high-speed data storage. However, since it requires bi-directional domain wall (DW) shifting process for outputting data, the mainstream stripe-shaped concept certainly suffers from the data overflow issue. This geometrical restriction leads to increasing complexity of peripheral circuits or programming as well as undesirable reliability issue. In this work, we propose and study ring-shaped RM, which is based on an alternative mechanism, spin orbit torque (SOT) driven chiral DW motions. Micromagnetic simulations have been carried out to validate its functionality and exhibit its performance advantages. The current flowing through the heavy metal instead of ferromagnetic layer realizes the "end to end" circulation of storage data, which remains all the data in the device even if they are shifted. It blazes a promising path for application of RM in practical memory and logic.

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work.

Voltage Controlled Magnetic Skyrmion Motion for Racetrack Memory.

Magnetic skyrmion, vortex-like swirling topologically stable spin configurations, is appealing as information carrier for future nanoelectronics, owing to the stability, small size and extremely low driving current density. One of the most promising applications of skyrmion is to build racetrack memory (RM). Compared to domain wall-based RM (DW-RM), skyrmion-based RM (Sky-RM) possesses quite a few benefits in terms of energy, density and speed etc. Until now, the fundamental behaviors, including nucleation/annihilation, motion and detection of skyrmion have been intensively investigated. However, one indispensable function, i.e., pinning/depinning of skyrmion still remains an open question and has to be addressed before applying skyrmion for RM. Furthermore, Current research mainly focuses on physical investigations, whereas the electrical design and evaluation are still lacking. In this work, we aim to promote the development of Sky-RM from fundamental physics to realistic electronics. First, we investigate the pinning/depinning characteristics of skyrmion in a nanotrack with the voltage-controlled magnetic anisotropy (VCMA) effect. Then, we propose a compact model and design framework of Sky-RM for electrical evaluation. This work completes the elementary memory functionality of Sky-RM and fills the technical gap between the physicists and electronic engineers, making a significant step forward for the development of Sky-RM.

Origin of interfacial perpendicular magnetic anisotropy in MgO/CoFe/metallic capping layer structures.

Spin-transfer-torque magnetic random access memory (STT-MRAM) attracts extensive attentions due to its non-volatility, high density and low power consumption. The core device in STT-MRAM is CoFeB/MgO-based magnetic tunnel junction (MTJ), which possesses a high tunnel magnetoresistance ratio as well as a large value of perpendicular magnetic anisotropy (PMA). It has been experimentally proven that a capping layer coating on CoFeB layer is essential to obtain a strong PMA. However, the physical mechanism of such effect remains unclear. In this paper, we investigate the origin of the PMA in MgO/CoFe/metallic capping layer structures by using a first-principles computation scheme. The trend of PMA variation with different capping materials agrees well with experimental results. We find that interfacial PMA in the three-layer structures comes from both the MgO/CoFe and CoFe/capping layer interfaces, which can be analyzed separately. Furthermore, the PMAs in the CoFe/capping layer interfaces are analyzed through resolving the magnetic anisotropy energy by layer and orbital. The variation of PMA with different capping materials is attributed to the different hybridizations of both d and p orbitals via spin-orbit coupling. This work can significantly benefit the research and development of nanoscale STT-MRAM.

Current-limiting challenges for all-spin logic devices.

All-spin logic device (ASLD) has attracted increasing interests as one of the most promising post-CMOS device candidates, thanks to its low power, non-volatility and logic-in-memory structure. Here we investigate the key current-limiting factors and develop a physics-based model of ASLD through nano-magnet switching, the spin transport properties and the breakdown characteristic of channel. First, ASLD with perpendicular magnetic anisotropy (PMA) nano-magnet is proposed to reduce the critical current (Ic0). Most important, the spin transport efficiency can be enhanced by analyzing the device structure, dimension, contact resistance as well as material parameters. Furthermore, breakdown current density (JBR) of spin channel is studied for the upper current limitation. As a result, we can deduce current-limiting conditions and estimate energy dissipation. Based on the model, we demonstrate ASLD with different structures and channel materials (graphene and copper). Asymmetric structure is found to be the optimal option for current limitations. Copper channel outperforms graphene in term of energy but seriously suffers from breakdown current limit. By exploring the current limit and performance tradeoffs, the optimization of ASLD is also discussed. This benchmarking model of ASLD opens up new prospects for design and implementation of future spintronics applications.