Neuromorphic Spintronics

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses

Multifunctional hardware technologies for neuromorphic computing are essential for replicating the complexity of biological neural systems, thereby improving the performance of artificial synapses and neurons. Integrating ionic and spintronic technologies offers new degrees of freedom to modulate synaptic potentiation and depression, introducing novel magnetic functionalities alongside the established ionic analogue behavior. We demonstrate that magneto-ionic devices can perform as synaptic elements with dynamically tunable depression linearity controlled by an external magnetic field, a functionality reminiscent of neuromodulation in biological systems. By applying magnetic fields we significantly reduce the nonlinearity of synaptic depression, transitioning from an exponential dependence to a linear response at higher fields. Neural network simulations reveal that this magnetically induced linearity enhancement improves learning accuracy across a wide range of learning rates, which is retained after the magnetic field is removed. These findings highlight the versatility and promise of magneto-ionic devices for developing tunable synaptic elements for neuromorphic hardware.

Resolving and routing magnetic polymorphs in a 2D layered antiferromagnet

Polymorphism, commonly denoting diverse molecular or crystal structures, is crucial in the natural sciences. In van der Waals antiferromagnets, a new type of magnetic polymorphism arises, presenting multiple layer-selective magnetic structures with identical total magnetization. However, resolving and manipulating such magnetic polymorphs remain challenging. Here, phase-resolved magnetic second harmonic generation microscopy is used to elucidate magnetic polymorphism in 2D layered antiferromagnet CrSBr, demonstrating deterministic and layer-selective switching of magnetic polymorphs. Using a nonlinear magneto-optical technique, we unambiguously resolve the polymorphic spin-flip transitions in CrSBr bilayers and tetralayers through both the amplitude and phase of light. Remarkably, the deterministic routing of polymorphic spin-flip transitions originates from a 'layer-sharing' effect, where the transitions are governed by laterally extended layers acting as 'control bits'. We envision that such controllable magnetic polymorphism could be ubiquitous for van der Waals layered antiferromagnets, enabling new designs and constructions of spintronic and opto-spintronic devices for probabilistic computation and neuromorphic engineering.

Active Inference and Artificial Spin Ice: Control Processes and State Selection

Theory and simulations are used to demonstrate implementation of a variational Bayes algorithm called "active inference" in interacting arrays of nanomagnetic elements. The algorithm requires stochastic elements, and a simplified model based on a magnetic artificial spin ice geometry is used to illustrate how nanomagnets can generate the required random dynamics. Examples of tracking and PID control are demonstrated and shown to be consistent with the original stochastic differential equation formulation of active inference. Interestingly, nonlinear response in the form of spikes and spike trains not predicted by the original theory can appear in the nanomagnet system for certain temperature regimes. A theoretical approach using a mean-field approximation for spin systems is proposed, which describes the transition to nonlinear response. Finally, the possibility to create simple magnetic arrays using realistic models is shown with micromagnetic simulations of a simple 17 element array of nanomagnets that include magnetic anisotropies, and exchange and dipolar interactions. Possible applications are simulated to illustrate how nanomagnetic arrays can be used as the stochastic element for feedback control of processes, investigation and control of magnetic state evolution, and as a method to optimize pulsed field magnetic switching protocols.

Quantum-limited stochastic optical neural networks operating at a few quanta per activation

Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10), and the noise can be treated as a perturbation. We study optical neural networks where all layers except the last are operated in the limit that each neuron can be activated by just a single photon, and as a result the noise on neuron activations is no longer merely perturbative. We show that by using a physics-based probabilistic model of the neuron activations in training, it is possible to perform accurate machine-learning inference in spite of the extremely high shot noise (SNR  ~ 1). We experimentally demonstrated MNIST handwritten-digit classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to just 0.038 photons per multiply-accumulate (MAC) operation. Our physics-aware stochastic training approach might also prove useful with non-optical ultra-low-power hardware.

Nonlinear Optics in Two-Dimensional Magnetic Materials: Advancements and Opportunities

Nonlinear optics, a critical branch of modern optics, presents unique potential in the study of two-dimensional (2D) magnetic materials. These materials, characterized by their ultra-thin geometry, long-range magnetic order, and diverse electronic properties, serve as an exceptional platform for exploring nonlinear optical effects. Under strong light fields, 2D magnetic materials exhibit significant nonlinear optical responses, enabling advancements in novel optoelectronic devices. This paper outlines the principles of nonlinear optics and the magnetic structures of 2D materials, reviews recent progress in nonlinear optical studies, including magnetic structure detection and nonlinear optical imaging, and highlights their role in probing magnetic properties by combining second harmonic generation (SHG) and multispectral integration. Finally, we discuss the prospects and challenges for applying nonlinear optics to 2D magnetic materials, emphasizing their potential in next-generation photonic and spintronic devices.

Spintronic devices and applications using noncollinear chiral antiferromagnets

Antiferromagnetic materials have several unique properties, such as a vanishingly small net magnetization, which generates weak dipolar fields and makes them robust against perturbation from external magnetic fields and rapid magnetization dynamics, as dictated by the geometric mean of their exchange and anisotropy energies. However, experimental and theoretical techniques to detect and manipulate the antiferromagnetic order in a fully electrical manner must be developed to enable advanced spintronic devices with antiferromagnets as their active spin-dependent elements. Among the various antiferromagnetic materials, conducting antiferromagnets offer high electrical and thermal conductivities and strong electron-spin-phonon interactions. Noncollinear metallic antiferromagnets with negative chirality, including Mn3Sn, Mn3Ge, and Mn3GaN, offer rich physics of spin momentum locking, topologically protected surface states, large spin Hall conductivity, and a magnetic spin Hall effect that arises from their topology. In this review article, we introduce the crystal structure and the physical phenomena, including the anomalous Hall and Nernst effects, spin Hall effect, and magneto-optic Kerr effect, observed in negative chirality antiferromagnets. Experimental advances related to spin-orbit torque-induced dynamics and the impact of the torque on the microscopic spin structure of Mn3Sn are also discussed. Recent experimental demonstrations of a finite room-temperature tunneling magnetoresistance in tunnel junctions with chiral antiferromagnets opens the prospect of developing spintronic devices with fully electrical readout. Applications of chiral antiferromagnets, including non-volatile memory, high-frequency signal generators/detectors, neuro-synaptic emulators, probabilistic bits, thermoelectric devices, and Josephson junctions, are highlighted. We also present analytic models that relate the performance characteristics of the device with its design parameters, thus enabling a rapid technology-device assessment. Effects of Joule heating and thermal noise on the device characteristics are briefly discussed. We close the paper by summarizing the status of research and present our outlook in this rapidly evolving research field.

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

Physical neural networks (PNN) using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training PNN is difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e., training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via an autonomous physical process, eliminating the requirements on external computation resources. Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN), and unsupervised learning is intrinsically performed via the evolution of the physical process. Using magnetic texture-defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform toward efficient autonomous training directly in materials.

Persistent ferromagnetic ground state in pristine and Ni-doped Fe3GaTe2 flakes

Room-temperature magnetism and its stability upon miniaturization are essential characteristics required for materials for spintronic devices and information storage. Among various candidates, Fe3GaTe2 stands out due to its high Curie temperature and strong perpendicular magnetic anisotropy (PMA), recently gaining large attention as one of the promising candidate materials for spintronics applications. In this study, we measured the thickness-dependent ferromagnetic properties of Fe3GaTe2 and (Fe1 - xNix)3GaTe2 (with x = 0.1) flakes. We observed that both pristine and Ni-doped Fe3GaTe2 exhibit persistent ferromagnetism, with only a minor decrease in TC as the thickness is reduced to a few tens of nanometers. This capacity to retain robust ferromagnetic properties at reduced dimensions is highly advantageous for thin-film applications, which is crucial for the scaling of spintronic devices. Understanding and controlling thickness-dependent magnetic properties is fundamental to harnessing the full potential of Fe3GaTe2 in van der Waals magnetic heterostructures and advanced spintronic technologies.

Computing with oscillators from theoretical underpinnings to applications and demonstrators

Networks of coupled oscillators have far-reaching implications across various fields, providing insights into a plethora of dynamics. This review offers an in-depth overview of computing with oscillators covering computational capability, synchronization occurrence and mathematical formalism. We discuss numerous circuit design implementations, technology choices and applications from pattern retrieval, combinatorial optimization problems to machine learning algorithms. We also outline perspectives to broaden the applications and mathematical understanding of coupled oscillator dynamics.

Antiferromagnetic spin-torque diode effect in a kagome Weyl semimetal

Spintronics based on ferromagnets has enabled the development of microwave oscillators and diodes. To achieve even faster operation, antiferromagnets hold great promise despite their challenging manipulation. So far, controlling antiferromagnetic order with microwave currents remains elusive. Here we induce the coherent rotation of antiferromagnetic spins in a Weyl antiferromagnet W/Mn3Sn epitaxial bilayer by DC spin-orbit torque. We show the efficient coupling of this spin rotation with microwave current. The coupled dynamics produce a DC anomalous Hall voltage through rectification, which we coin the antiferromagnetic spin-torque diode effect. Unlike in ferromagnetic systems, the output voltage shows minimal dependence on frequency because of the stabilization of the precession cone angle by exchange interactions. Between 10 GHz and 30 GHz, the output voltage decreases by only 10%. Numerical simulations further reveal that the rectification signals arise from the fast frequency modulation of chiral spin rotation by microwave spin-orbit torque. These results may help the development of high-speed microwave devices for next-generation telecommunication applications.

All-in-One Magneto-optical Memory Arrays Based on a Two-Dimensional Ferromagnetic Metal

Two-dimensional (2D) van der Waals (vdW) magnetic materials with atomic-scale thickness and smooth interfaces promise the possibility of developing high-density, energy-efficient spintronic devices. However, it remains a challenge to effectively control the perpendicular magnetic anisotropy (PMA) of 2D vdW ferromagnetic materials, as well as the integration of multiple memory cells. Here, we report highly efficient magneto-optical memory arrays by utilizing the huge spin-orbit torques (SOT) induced by the in-plane current in Fe3GeTe2 (FGT) flake. The device is constructed from individual FGT flakes without heavy metal assistance and allows for a low current density. The magneto-optical memory arrays implement nonvolatile memories for three bits and can be repeatedly scrubbed for "writing" and "reading". Besides, we show that FGT nanoflakes possess current-controlled volatile switching behavior at zero magnetic field. These results provide a solution for the next generation of all-vdW-scalable, high-performance spintronic logic devices and SOT-Magnetic Random Access Memory (MRAM).

Understanding the Spin of Metal Complexes from a Single-Molecule Perspective

Compared with aggregate spin behavior, single-molecule spin behavior can be accurately understood, controlled, and applied at the level of basic building blocks. The potential of single-molecule electronic and nuclear spins for monitoring and control represents a beacon of promise for the advancement of molecular spin devices, which are fabricated by connecting a single molecule between two electrodes. Metal complexes, celebrated for their superior magnetic attributes, are widely used in the devices to explore spin effects. Moreover, single-molecule electrical techniques with high signal-to-noise ratio, temporal resolution, and reliability help to understand the spin characteristics. In this review, the focus is on the devices with metal complexes, especially single-molecule magnets, and systematically present experimental and theoretical state of the art of this field at the single-molecule level, including the fundamental concepts of the electronic and nuclear spin and their basic spin effects. Then, several experimental methods developed to regulate the spin characteristics of metal complexes at single-molecule level are introduced, as well as the corresponding intrinsic mechanisms. A brief discussion is provided on the comprehensive applications and the considerable challenges of single-molecule spin devices in detail, along with a prospect on the potential future directions of this field.

Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use noise to represent uncertainties about the somatic activity of the postsynaptic neuron. To show this, we developed a mathematical framework, in which the synapse as a whole interacts with the soma of the postsynaptic neuron in a similar way to an agent that is situated and behaves in an uncertain, dynamic environment. This framework suggests that synapses use an implicit internal model of the somatic membrane dynamics that is being updated by a synaptic learning rule, which resembles experimentally well-established LTP/LTD mechanisms. In addition, this approach entails that a synapse utilizes its inherently noisy synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its postsynaptic neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate themselves to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.

Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

The ventral visual pathway (VVP) of the human brain efficiently implements target recognition by employing a deep hierarchical structure to build complex visual concepts from simple features. Artificial neural networks (ANNs) based on spintronic devices are capable of target recognition, but their poor interpretability and limited network depth hinder ANNs from mimicking the VVP. Hardware implementation of the VVP requires a biorealistic spintronic device as well as the corresponding interpretable and deep network structure, which have not been reported so far. Here, we report a ferrimagnetic neuron with a continuously differentiable exponential linear unit (CeLu) activation function, which is closer to biological neurons and could mitigate the issue of limited network depth. Meanwhile, we also demonstrate that a ferrimagnet can construct artificial synapses with high linearity and symmetry to meet the requirements of weight update algorithms. Based on these neurons and synapses, we propose an all-spin convolutional neural network (CNN) with a high interpretability and deep neural network, to mimic the VVP. Compared to the state-of-the-art spintronic-based neuromorphic computing model, the CNN with bionic function, using experimentally derived device parameters, achieves high recognition accuracies of over 91% and 98% on the CIFAR-10 datasets and the MNIST datasets, respectively, showing improvements of 1.13% and 1.76%. Our work provides a promising method to improve the bionic performance of spintronic device-based neural networks.

Multicore memristor from electrically readable nanoscopic racetracks

The manipulation and detection of mobile domain walls in nanoscopic magnetic wires underlies the development of multibit memories. The studies of such domain walls have focused on macroscopic wires that allow for optical detection by using magneto-optic effects. In this study, we demonstrated the electrical tracking with a spatial resolution of better than 40 nm of multiple mobile domain walls in nanoscopic racetracks, using a set of anomalous Hall detectors integrated into the racetracks. Electrical time-series signals from the Hall detectors allow for the static and dynamic phase space visualization of the dynamics of a domain wall or multiple domain walls that can be described by a multicore memristor model. The domain wall dynamics and stochasticity can be controlled in racetracks even to deep submicron dimensions.

Spin dependent tunneling and strain sensitivity in a Co2MnSb/HfIrSb magnetic tunneling junction: a first-principles study

Half-metallic Co-based full Heusler alloys have captured considerable attention of researchers in the realm of spintronic applications, owing to their remarkable characteristics such as exceptionally high spin polarization at the Fermi level, ultra-low Gilbert damping, and a high Curie temperature. In this comprehensive study, employing the density functional theory, we delve into the electronic stability and ballistic spin transport properties of a magnetic tunneling junction (MTJ) comprising a Co2MnSb/HfIrSb interface. An in-depth investigation of k-dependent spin transmissions uncovers the occurrence of coherent tunneling for the Mn-Mn/Ir interface, particularly when a spacer layer beyond a certain thickness is employed. It has been found that the Co-terminated Co2MnSb/HfIrSb interface shows perpendicular magnetic anisotropy, while those with Mn-Sb and Mn-Mn termination exhibit in-plane magnetic anisotropy. Furthermore, our spin-dependent transmission calculations demonstrate that the Mn-Mn/Ir interface manifests strain-sensitive transmission properties under both compressive and tensile strain and yields a remarkable three-fold increase in majority spin transmission under tensile strain conditions. We find a tunnel magnetoresistance of ∼500% under a bi-axial strain of -3%, beyond which the tunnel resistance is found to be theoretically infinite. These compelling outcomes place the Co2MnSb/HfIrSb junction among the highly promising candidates for nanoscale spintronic devices, emphasizing the potential significance of the system in the advancement of the field.

Current-controlled periodic double-polarity reversals in a spin-torque vortex oscillator

Micromagnetic simulations are used to study a spin-torque vortex oscillator excited by an out-of-plane dc current. The vortex core gyration amplitude is confined between two orbits due to periodical vortex core polarity reversals. The upper limit corresponds to the orbit where the vortex core reaches its critical velocity triggering the first polarity reversal which is immediately followed by a second one. After this double polarity reversal, the vortex core is on a smaller orbit that defines the lower limit of the vortex core gyration amplitude. This double reversal process is a periodic phenomenon and its frequency, as well as the upper and lower limit of the vortex core gyration, is controlled by the input current density while the vortex chirality determines the apparition of this confinement regime. In this non-linear regime, the vortex core never reaches a stable orbit and thus, it can be of interest for neuromorphic application as a leaky integrate-and-fire neuron for example.

Neural Functions Enabled by a Polarity-Switchable Nanofluidic Memristor

Reproducing neural functions with artificial nanofluidic systems has long been an aspirational goal for neuromorphic computing. In this study, neural functions, such as neural activation and synaptic plasticity, are successfully accomplished with a polarity-switchable nanofluidic memristor (PSNM), which is based on the anodized aluminum oxide (AAO) nanochannel array. The PSNM has unipolar memristive behavior at high electrolyte concentrations and bipolar memristive behavior at low electrolyte concentrations, which can emulate neural activation and synaptic plasticity, respectively. The mechanisms for the unipolar and bipolar memristive behaviors are related to the polyelectrolytic Wien (PEW) effect and ion accumulation/depletion effect, respectively. These findings are beneficial to the advancement of neuromorphic computing on nanofluidic platforms.

Photoresponsive Two-Dimensional Magnetic Junctions for Reconfigurable In-Memory Sensing

Magnetic tunneling junctions (MTJs) lie in the core of magnetic random access memory, holding promise in integrating memory and computing to reduce hardware complexity, transition latency, and power consumption. However, traditional MTJs are insensitive to light, limiting their functionality in in-memory sensing─a crucial component for machine vision systems in artificial intelligence applications. Herein, the convergence of magnetic memory with optical sensing capabilities is achieved in the all-two-dimensional (2D) magnetic junction Fe3GaTe2/WSe2/Fe3GaTe2, which combines 2D magnetism and optoelectronic properties. The clean intrinsic band gap and prominent photoresponse of interlayer WSe2 endow the tunneling barrier with optical tunability. The on-off states of junctions and the magnetoresistance can be flexibly controlled by the intensity of the optical signal at room temperature. Based on the optical-tunable magnetoresistance in all-2D magnetic junctions, a machine vision system with the architecture of in-memory sensing and computing is constructed, which possesses high performance in image recognition. Our work exhibits the advantages of 2D magneto-electronic devices and extends the application scenarios of magnetic memory devices in artificial intelligence.

Pattern recognition using spiking antiferromagnetic neurons

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

Chiral spin-transfer torque induced by curvature gradient

This work analyzes the propagation of a transverse domain wall (DW) under the action of an electric current along a nanowire with a curvature gradient. Our results evidence that the curvature gradient induces a chiral spin-transfer torque (CSTT) whose effect on the DW dynamics depends on the direction along which the DW points, evidencing a curvature-induced non-reciprocity in the current-driven DW motion. The origin of the CSTT is explained in terms of a position-dependent effective field associated with the DW profile and the electric current direction. This current-driven chiral effect is responsible for direction-dependent reinforcing or blocking the DW propagation. The emergence of curvature-induced chiral spin transport is a phenomenon to consider when designing spintronic devices.

Electrical Control of Magnetic Resonance in Phase Change Materials

Metal-insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in the spin excitations are also expected. The transition metal oxide La0.7Sr0.3MnO3 (LSMO) is a ferromagnetic metal at low temperatures and a paramagnetic insulator above room temperature. When LSMO is in its metallic phase, a critical electrical bias has been shown to lead to an MIT that results in the formation of a paramagnetic resistive barrier transverse to the applied electric field. Using spin-transfer ferromagnetic resonance spectroscopy, we show that even for electrical biases less than the critical value that triggers the MIT, there is magnetic phase separation, with the spin-excitation resonances varying systematically with applied bias. Therefore, voltage-triggered MITs in LSMO can alter magnetic resonance characteristics, offering an effective method for tuning synaptic weights in neuromorphic circuits.

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Nonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose-Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high-frequency resolution remains a challenge. Here, we demonstrate the generation and detection of phase-coherent nonlinear magnons in an all-electrical GHz probe station based on coplanar waveguides connected to a vector network analyzer which we operate in a frequency-offset mode. Making use of an unprecedented frequency resolution, we resolve the nonlocal emergence of a fine structure of propagating nonlinear magnons, which sensitively depends on both power and a magnetic field. These magnons are shown to maintain coherency with the microwave source while propagating over macroscopic distances. We propose a multi-band four-magnon scattering scheme that is in agreement with the field-dependent characteristics of coherent nonlocal signals in the nonlinear excitation regime. Our findings are key to enable the seamless integration of nonlinear magnon processes into high-speed microwave electronics and to advance phase-encoded information processing in magnonic neuronal networks.

A Flexible Memristor Based on CsPbCl3 Nanocrystals for an Artificial Nociceptor

Halide perovskites (HPs) based memristors show great potential in the simulation of biological neurons. Herein, a memristor with Ag/PMMA&CsPbCl3/ITO structure is developed by incorporating CsPbCl3 nanocrystals (NCs) into poly(methyl methacrylate) (PMMA) as the functional layer. The device exhibits typical bipolar resistive behavior, low operating voltage, good endurance of more than 400 cycles, consistent and excellent ON/OFF ratio (≈ 103), and high mechanical bending stability (bending times = 1000). The RS mechanism has been well explained by the electric field induced formation and rupture of Ag filaments in the PMMA&CsPbCl3 layer. More importantly, the memristor successfully displays fundamental nociceptive functions including threshold, nonadaptation, relaxation, and sensitization (allodynia and hyperalgesia). To demonstrate the feasibility of the artificial nociceptor, a pressure nociceptor system is constructed using the Ag/PMMA&CsPbCl3/ITO device. These results provide new perspectives for the development of next-generation, high-performance HPs based neural morphology devices.

Bimodal alteration of cognitive accuracy for spintronic artificial neural networks

Spintronics-based artificial neural networks (ANNs) exhibiting nonvolatile, fast, and energy-efficient computing capabilities are promising neuromorphic hardware for performing complex cognitive tasks of artificial intelligence and machine learning. Early experimental efforts focused on multistate device concepts to enhance synaptic weight precisions, albeit compromising on cognitive accuracy due to their low magnetoresistance. Here, we propose a hybrid approach based on the tuning of tunnel magnetoresistance (TMR) and the number of states in the compound magnetic tunnel junctions (MTJs) to improve the cognitive performance of an all-spin ANN. A TMR variation of 33-78% is controlled by the free layer (FL) thickness wedge (1.6-2.6 nm) across the wafer. Meanwhile, the number of resistance states in the compound MTJ is manipulated by varying the number of constituent MTJ cells (n = 1-3), generating n + 1 states with a TMR difference between consecutive states of at least 21%. Using MNIST handwritten digit and fashion object databases, the test accuracy of the compound MTJ ANN is observed to increase with the number of intermediate states for a fixed FL thickness or TMR. Meanwhile, the test accuracy for a 1-cell MTJ increases linearly by 8.3% and 7.4% for handwritten digits and fashion objects, respectively, with increasing TMR. Interestingly, a multifarious TMR dependence of test accuracy is observed with the increasing synaptic complexity in the 2- and 3-cell MTJs. By leveraging on the bimodal tuning of multilevel and TMR, we establish viable paths for enhancing the cognitive performance of spintronic ANN for in-memory and neuromorphic computing.

Realization of skyrmion shift register

The big data explosion demands novel data storage technology. Among many different approaches, solitonic racetrack memory devices hold great promise for accommodating nonvolatile and low-power functionalities. As representative topological solitons, magnetic skyrmions are envisioned as potential information carriers for efficient information processing. While their advantages as memory and logic elements have been vastly exploited from theoretical perspectives, the corresponding experimental efforts are rather limited. These challenges, which are key to versatile skyrmionic devices, will be studied in this work. Through patterning concaved surface topography with designed arrays of indentations on standard Si/SiO2 substrates, we demonstrate that the resultant non-flat energy landscape could lead to the formation of hexagonal and square skyrmion lattices in Ta/CoFeB/MgO multilayers. Based on these films, one-dimensional racetrack devices are subsequently fabricated, in which a long-distance deterministic shifting of skyrmions between neighboring indentations is achieved at room temperature. Through separating the word line and the bit line, a prototype shift register device, which can sequentially generate and precisely shift complex skyrmionic data strings, is presented. The deterministic writing and long-distance shifting of skyrmionic bits can find potential applications in transformative skyrmionic memory, logic as well as the in-memory computing devices.

Collective dynamics and long-range order in thermal neuristor networks

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Energy-efficient synthetic antiferromagnetic skyrmion-based artificial neuronal device

Magnetic skyrmions offer unique characteristics such as nanoscale size, particle-like behavior, topological stability, and low depinning current density. These properties make them promising candidates for next-generation spintronics-based memory and neuromorphic computing. However, one of their distinctive features is their tendency to deviate from the direction of the applied driving force that may lead to the skyrmion annihilation at the edge of nanotrack during skyrmion motion, known as the skyrmion Hall effect (SkHE). To overcome this problem, synthetic antiferromagnetic (SAF) skyrmions that having bilayer coupling effect allows them to follow a straight path by nullifying SkHE making them alternative for ferromagnetic (FM) counterpart. This study proposes an integrate-and-fire (IF) artificial neuron model based on SAF skyrmions with asymmetric wedge-shaped nanotrack having self-sustainability of skyrmion numbers at the device window. The model leverages inter-skyrmion repulsion to replicate the IF mechanism of biological neuron. The device threshold, determined by the maximum number of pinned skyrmions at the device window, can be adjusted by tuning the current density applied to the nanotrack. Neuronal spikes occur when initial skyrmion reaches the detection unit after surpassing the device window by the accumulation of repulsive force that result in reduction of the device's contriving current results to design of high energy efficient for neuromorphic computing. Furthermore, work implements a binarized neuronal network accelerator using proposed IF neuron and SAF-SOT-MRAM based synaptic devices for national institute of standards and technology database image classification. The presented approach achieves significantly higher energy efficiency compared to existing technologies like SRAM and STT-MRAM, with improvements of 2.31x and 1.36x, respectively. The presented accelerator achieves 1.42x and 1.07x higher throughput efficiency per Watt as compared to conventional SRAM and STT-MRAM based designs.

Phase and frequency-resolved microscopy of operating spin Hall nano-oscillator arrays

Coherent optical detection is a powerful technique for characterizing a wide range of physical excitations. Here, we use two optical approaches (fundamental and parametric pumping) to microscopically characterize the high-frequency auto-oscillations of single and multiple nano-constriction spin Hall nano-oscillators (SHNOs). To validate the technique and demonstrate its robustness, we study SHNOs made from two different material stacks, NiFe/Pt and W/CoFeB/MgO, and investigate the influence of both the RF injection power and the laser power on the measurements, comparing the optical results to conventional electrical measurements. To demonstrate the key features of direct, non-invasive, submicron, spatial, and phase-resolved characterization of the SHNO magnetodynamics, we map out the auto-oscillation magnitude and phase of two phase-binarized SHNOs used in Ising machines. This proof-of-concept platform establishes a strong foundation for further extensions, contributing to the ongoing development of crucial characterization techniques for emerging computing technologies based on spintronics devices.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Experimental observation of current-driven antiskyrmion sliding in stripe domains

Magnetic skyrmions are promising as next-generation information units. Their antiparticle-the antiskyrmion-has also been discovered in chiral magnets. Here we experimentally demonstrate antiskyrmion sliding in response to a pulsed electric current at room temperature without the requirement of an external magnetic field. This is realized by embedding antiskyrmions in helical stripe domains, which naturally provide one-dimensional straight tracks along which antiskyrmion sliding can be easily launched with low current density and without transverse deflection from the antiskyrmion Hall effect. The higher mobility of the antiskyrmions in the background of helical stripes in contrast to the typical ferromagnetic state is a result of intrinsic material parameters and elastic energy of the stripe domain, thereby smearing out the random pinning potential, as supported by micromagnetic simulations. The demonstration and comprehensive understanding of antiskyrmion movement along naturally straight tracks offers a new perspective for (anti)skyrmion application in spintronics.

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Beyond 25 years of biomedical innovation in nano-bioelectronics

Nano-bioelectronics, which blend the precision of nanotechnology with the complexity of biological systems, are evolving with innovations such as silicon nanowires, carbon nanotubes, and graphene. These elements serve applications from biochemical sensing to brain-machine interfacing. This review examines nano-bioelectronics' role in advancing biomedical interventions and discusses their potential in environmental monitoring, agricultural productivity, energy efficiency, and creative fields. The field is transitioning from molecular to ecosystem-level applications, with research exploring complex cellular mechanisms and communication. This fosters understanding of biological interactions at various levels, such as suggesting transformative approaches for ecosystem management and food security. Future research is expected to focus on refining nano-bioelectronic devices for integration with biological systems and on scalable manufacturing to broaden their reach and functionality.

Influence of Magnetic Sublattice Ordering on Skyrmion Bubble Stability in 2D Magnet Fe5GeTe2

The realization of above room-temperature ferromagnetism in the two-dimensional (2D) magnet Fe5GeTe2 represents a major advance for the use of van der Waals (vdW) materials in practical spintronic applications. In particular, observations of magnetic skyrmions and related states within exfoliated flakes of this material provide a pathway to the fine-tuning of topological spin textures via 2D material heterostructure engineering. However, there are conflicting reports as to the nature of the magnetic structures in Fe5GeTe2. The matter is further complicated by the study of two types of Fe5GeTe2 crystals with markedly different structural and magnetic properties, distinguished by their specific fabrication procedure: whether they are slowly cooled or rapidly quenched from the growth temperature. In this work, we combine X-ray and electron microscopy to observe the formation of magnetic stripe domains, skyrmion-like type-I, and topologically trivial type-II bubbles, within exfoliated flakes of Fe5GeTe2. The results reveal the influence of the magnetic ordering of the Fe1 sublattice below 150 K, which dramatically alters the magnetocrystalline anisotropy and leads to a complex magnetic phase diagram and a sudden change of the stability of the magnetic textures. In addition, we highlight the significant differences in the magnetic structures intrinsic to slow-cooled and quenched Fe5GeTe2 flakes.

Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors

Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

Strain-mediated multistate skyrmion for neuron devices

Magnetic skyrmions are potential candidates for neuromorphic computing because of their inherent topological stability, low drive current density and nanoscale size. However, an artificial neuron device based on current-driven skyrmion motion cannot satisfy the requirement of energy efficiency and integration density due to hundreds of millions of interconnected neurons and synapses present in the deep networks. Here, we present a compact and energy efficient skyrmion-based artificial neuron consisting of ferromagnetic/heavy metal/ferroelectric layers which uses strain-mediated voltage manipulation of skyrmion states to mimic the Integrate-and-Fire (IF) function of biological neurons. By implementation of a spiking neural network (SNN) based on the proposed skyrmionic neuronal devices, it can achieve a high accuracy of 95.08% on a modified National Institute of Standards and Technology (MNIST) handwritten digit dataset, as well as a low power consumption of ∼46.8 fJ per epoch per neuron. The present work suggests a novel way to realize energy-efficient and high-density neuromorphic computing.

Neuromorphic Computing with Emerging Antiferromagnetic Ordering in Spin-Orbit Torque Devices

Field-free switching (FFS) and spin-orbit torque (SOT)-based neuromorphic characteristics were realized in a W/Pt/Co/NiO/Pt heterostructure with a perpendicular exchange bias (HEB) for brain-inspired neuromorphic computing (NC). Experimental results using NiO-based SOT devices guided the development of fully spin-based artificial synapses and sigmoidal neurons for implementation in a three-layer artificial neural network. This system achieved impressive accuracies of 91-96% when applied to the Modified National Institute of Standards and Technology (MNIST) image data set and 78.85-81.25% when applied to Fashion MNIST images, due presumably to the emergence of robust NiO antiferromagnetic (AFM) ordering. The emergence of AFM ordering favored the FFS with an enhanced HEB, which suppressed the memristivity and reduced the recognition accuracy. This indicates a trade-off between the requirements for solid-state memory and those required for brain-inspired NC devices. Nonetheless, our findings revealed opportunities by which the two technologies could be aligned via controllable exchange coupling.

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Neuromorphic nanoelectronic devices that can emulate the temperature-sensitive dynamics of biological neurons are of great interest for bioinspired robotics and advanced applications such as in silico neuroscience. In this work, we demonstrate the biomimetic thermosensitive properties of two-terminal V3O5 memristive devices and showcase their similarity to the firing characteristics of thermosensitive biological neurons. The temperature-dependent electrical characteristics of V3O5-based memristors are used to understand the spiking response of a simple relaxation oscillator. The temperature-dependent dynamics of these oscillators are then compared with those of biological neurons through numerical simulations of a conductance-based neuron model, the Morris-Lecar neuron model. Finally, we demonstrate a robust neuromorphic thermosensation system inspired by biological thermoreceptors for bioinspired thermal perception and representation. These results not only demonstrate the biorealistic emulative potential of threshold-switching memristors but also establish V3O5 as a functional material for realizing solid-state neurons for neuromorphic computing and sensing applications.

Probability-Distribution-Configurable True Random Number Generators Based on Spin-Orbit Torque Magnetic Tunnel Junctions

The incorporation of randomness into stochastic computing can provide ample opportunities for applications such as simulated annealing, non-polynomial hard problem solving, and Bayesian neuron networks. In these cases, a considerable number of random numbers with an accurate and configurable probability distribution function (PDF) are indispensable. Preferably, these random numbers are provided at the hardware level to improve speed, efficiency, and parallelism. In this paper, how spin-orbit torque magnetic tunnel junctions (SOT-MTJs) with high barriers are suitable candidates for the desired true random number generators is demonstrated. Not only do these SOT-MTJs perform excellently in speed and endurance, but their randomness can also be conveniently and precisely controlled by a writing voltage, which makes them a well-performed Bernoulli bit. By utilizing these SOT-MTJ-based Bernoulli bits, any PDF, including Gaussian, uniform, exponential, Chi-square, and even arbitrarily defined distributions can be realized. These PDF-configurable true random number generators can then promise to advance the development of stochastic computing and broaden the applications of the SOT-MTJs.

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

Restricted Boltzmann Machines Implemented by Spin-Orbit Torque Magnetic Tunnel Junctions

Artificial intelligence has surged forward with the advent of generative models, which rely heavily on stochastic computing architectures enhanced by true random number generators with adjustable sampling probabilities. In this study, we develop spin-orbit torque magnetic tunnel junctions (SOT-MTJs), investigating their sigmoid-style switching probability as a function of the driving voltage. This feature proves to be ideally suited for stochastic computing algorithms such as the restricted Boltzmann machines (RBM) prevalent in pretraining processes. We exploit SOT-MTJs as both stochastic samplers and network nodes for RBMs, enabling the implementation of RBM-based neural networks to achieve recognition tasks for both handwritten and spoken digits. Moreover, we further harness the weights derived from the preceding image and speech training processes to facilitate cross-modal learning from speech to image generation. Our results clearly demonstrate that these SOT-MTJs are promising candidates for the development of hardware accelerators tailored for Boltzmann neural networks and other stochastic computing architectures.

Spatially reconfigurable antiferromagnetic states in topologically rich free-standing nanomembranes

Antiferromagnets hosting real-space topological textures are promising platforms to model fundamental ultrafast phenomena and explore spintronics. However, they have only been epitaxially fabricated on specific symmetry-matched substrates, thereby preserving their intrinsic magneto-crystalline order. This curtails their integration with dissimilar supports, restricting the scope of fundamental and applied investigations. Here we circumvent this limitation by designing detachable crystalline antiferromagnetic nanomembranes of α-Fe2O3. First, we show-via transmission-based antiferromagnetic vector mapping-that flat nanomembranes host a spin-reorientation transition and rich topological phenomenology. Second, we exploit their extreme flexibility to demonstrate the reconfiguration of antiferromagnetic states across three-dimensional membrane folds resulting from flexure-induced strains. Finally, we combine these developments using a controlled manipulator to realize the strain-driven non-thermal generation of topological textures at room temperature. The integration of such free-standing antiferromagnetic layers with flat/curved nanostructures could enable spin texture designs via magnetoelastic/geometric effects in the quasi-static and dynamical regimes, opening new explorations into curvilinear antiferromagnetism and unconventional computing.

Stable skyrmion bundles at room temperature and zero magnetic field in a chiral magnet

Topological spin textures are characterized by magnetic topological charges, Q, which govern their electromagnetic properties. Recent studies have achieved skyrmion bundles with arbitrary integer values of Q, opening possibilities for exploring topological spintronics based on Q. However, the realization of stable skyrmion bundles in chiral magnets at room temperature and zero magnetic field - the prerequisite for realistic device applications - has remained elusive. Here, through the combination of pulsed currents and reversed magnetic fields, we experimentally achieve skyrmion bundles with different integer Q values - reaching a maximum of 24 at above room temperature and zero magnetic field - in the chiral magnet Co8Zn10Mn2. We demonstrate the field-driven annihilation of high-Q bundles and present a phase diagram as a function of temperature and field. Our experimental findings are consistently corroborated by micromagnetic simulations, which reveal the nature of the skyrmion bundle as that of skyrmion tubes encircled by a fractional Hopfion.

Field-free deterministic switching of all-van der Waals spin-orbit torque system above room temperature

Two-dimensional van der Waals (vdW) magnetic materials hold promise for the development of high-density, energy-efficient spintronic devices for memory and computation. Recent breakthroughs in material discoveries and spin-orbit torque control of vdW ferromagnets have opened a path for integration of vdW magnets in commercial spintronic devices. However, a solution for field-free electric control of perpendicular magnetic anisotropy (PMA) vdW magnets at room temperatures, essential for building compact and thermally stable spintronic devices, is still missing. Here, we report a solution for the field-free, deterministic, and nonvolatile switching of a PMA vdW ferromagnet, Fe3GaTe2, above room temperature (up to 320 K). We use the unconventional out-of-plane anti-damping torque from an adjacent WTe2 layer to enable such switching with a low current density of 2.23 × 106 A cm-2. This study exemplifies the efficacy of low-symmetry vdW materials for spin-orbit torque control of vdW ferromagnets and provides an all-vdW solution for the next generation of scalable and energy-efficient spintronic devices.

All-electrical skyrmionic magnetic tunnel junction

Topological whirls or 'textures' of spins such as magnetic skyrmions represent the smallest realizable emergent magnetic entities1-5. They hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing6-8. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures9,10. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multimodal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal-20-70% relative to uniformly magnetized states-corresponds directly to skyrmion size. The MTJ exploits complementary nucleation mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three non-volatile electrical states. Crucially, it can electrically write and delete skyrmions to both uniform states with switching energies 1,000 times lower than the state of the art. Here, the applied voltage emulates a magnetic field and, in contrast to conventional MTJs, it reshapes both the energetics and kinetics of the switching transition, enabling deterministic bidirectional switching. Our stack platform enables large readout and efficient switching, and is compatible with lateral manipulation of skyrmionic bits, providing the much-anticipated backbone for all-electrical skyrmionic device architectures9,10. Its wafer-scale realizability provides a springboard to harness chiral spin textures for multibit memory and unconventional computing8,11.

Topological aspects of multi-kantiferromagnetism in cubic rare-earth compounds

We advertise rare-earth intermetallics with high-symmetry crystal structures and competing interactions as a possible materials platform hosting spin structures with non-trivial topological properties. Focusing on the series of cubicRCu compounds, whereR= Ho, Er, Tm, the bulk properties of these systems display exceptionally rich magnetic phase diagrams hosting an abundance of different phase pockets characteristic of antiferromagnetic order in the presence of delicately balanced interactions. The electrical transport properties exhibit large anomalous contributions suggestive of topologically non-trivial winding in the electronic and magnetic structures. Neutron diffraction identifies spontaneous long-range magnetic order in terms of commensurate and incommensurate variations of(ππ0)antiferromagnetism with the possibility for various multi-kconfigurations. Motivated by general trends in these materials, we discuss the possible existence of topologically non-trivial winding in real and reciprocal space in the class ofRCu compounds including antiferromagnetic skyrmion lattices. Putatively bringing together different limits of non-trivial topological winding in the same material, the combination of properties inRCu systems promises access to advanced functionalities.