Dimensionality Engineering of Magnetic Anisotropy from the Anomalous Hall Effect in Synthetic SrRuO3 Crystals.

Magnetic anisotropy in atomically thin correlated heterostructures is essential for exploring quantum magnetic phases for next-generation spintronics. Whereas previous studies have mostly focused on van der Waals systems, here we investigate the impact of dimensionality of epitaxially grown correlated oxides down to the monolayer limit on structural, magnetic, and orbital anisotropies. By designing oxide superlattices with a correlated ferromagnetic SrRuO3 and nonmagnetic SrTiO3 layers, we observed modulated ferromagnetic behavior with the change of the SrRuO3 thickness. Especially, for three-unit-cell-thick layers, we observe a significant 1500% improvement of the coercive field in the anomalous Hall effect, which cannot be solely attributed to the dimensional crossover in ferromagnetism. The atomic-scale heterostructures further reveal the systematic modulation of anisotropy for the lattice structure and orbital hybridization, explaining the enhanced magnetic anisotropy. Our findings provide valuable insights into engineering the anisotropic hybridization of synthetic magnetic crystals, offering a tunable spin order for various applications.

Demonstration of an energy-efficient Ising solver composed of Ovonic threshold switch (OTS)-based nano-oscillators (OTSNOs).

As there is an increasing need for an efficient solver of combinatorial optimization problems, much interest is paid to the Ising machine, which is a novel physics-driven computing system composed of coupled oscillators mimicking the dynamics of the system of coupled electronic spins. In this work, we propose an energy-efficient nano-oscillator, called OTSNO, which is composed of an Ovonic Threshold Switch (OTS) and an electrical resistor. We demonstrate that the OTSNO shows the synchronization behavior, an essential property for the realization of an Ising machine. Furthermore, we have discovered that the capacitive coupling is advantageous over the resistive coupling for the hardware implementation of an Ising solver by providing a larger margin of the variations of components. Finally, we implement an Ising machine composed of capacitively-coupled OTSNOs to demonstrate that the solution to a 14-node MaxCut problem can be obtained in 40 µs while consuming no more than 2.3 µJ of energy. Compared to a previous hardware implementation of the phase-transition nano-oscillator (PTNO)-based Ising machine, the OTSNO-based Ising machine in this work shows the performance of the increased speed by more than one order while consuming less energy by about an order.

Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity.

Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated in recent studies; however, large-scale implementation is challenging owing to device variations and the requirement of online parameter adaptation. This study proposes a neural connectivity inference method with one-dimensional spiking neurons using spike timing-dependent plasticity and presynaptic spike-driven spike timing-dependent plasticity learning rules, designed for a large-scale neuromorphic system. The proposed learning process decreases the number of spiking neurons by half. We simulate 12 ground-truth neural networks comprising one-dimensional eight and 64 neurons. We analyze the correlation between the neural connectivity of the ground truth and spiking neural networks using the Matthews correlation coefficient. In addition, we analyze the sensitivity and specificity of inference. Validation using the presynaptic spike-driven spike timing-dependent plasticity learning rule implies a potential approach for large-scale neural network inference with real hardware realization of large-scale neuromorphic systems.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Investigation of the mechanism of the anomalous Hall effects in Cr2Te3/(BiSb)2(TeSe)3 heterostructure.

The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the so-called topological Hall effect (THE) featuring a hump structure in the curve of the Hall resistance (Rxy) vs. a magnetic field (H) of a heterostructure consisting of a ferromagnet (FM) and a topological insulator (TI). The origin of the hump structure is still controversial between the topological Hall effect model and the multi-component anomalous Hall effect (AHE) model. In this work, we have investigated a heterostructure consisting of BixSb2-xTeySe3-y (BSTS) and Cr2Te3 (CT), which are well-known TI and two-dimensional FM, respectively. By using the so-called "minor-loop measurement", we have found that the hump structure observed in the CT/BSTS is more likely to originate from two AHE channels. Moreover, by analyzing the scaling behavior of each amplitude of two AHE with the longitudinal resistivities of CT and BSTS, we have found that one AHE is attributed to the extrinsic contribution of CT while the other is due to the intrinsic contribution of BSTS. It implies that the proximity-induced ferromagnetic layer inside BSTS serves as a source of the intrinsic AHE, resulting in the hump structure explained by the two AHE model.

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing.

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

Three-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory Neurons.

Inspired by information processing in biological systems, sensor-combined edge-computing systems attract attention requesting artificial sensory neurons as essential ingredients. Here, we introduce a simple and versatile structure of artificial sensory neurons based on a novel three-terminal Ovonic threshold switch (3T-OTS), which features an electrically controllable threshold voltage (Vth). Combined with a sensor driving an output voltage, this 3T-OTS generates spikes with a frequency depending on an external stimulus. As a proof of concept, we have built an artificial retinal ganglion cell (RGC) by combining a 3T-OTS and a photodiode. Furthermore, this artificial RGC is combined with the reservoir-computing technique to perform a classification of chest X-ray images for normal, viral pneumonia, and COVID-19 infections, releasing the recognition accuracy of about 86.5%. These results indicate that the 3T-OTS is highly promising for applications in neuromorphic sensory systems, providing a building block for energy-efficient in-sensor computing devices.

Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update.

Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric LTP and LTD curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.

A 2D material-based floating gate device with linear synaptic weight update.

Neuromorphic computing is of great interest among researchers interested in overcoming the von Neumann computing bottleneck. A synaptic device, one of the key components to realize a neuromorphic system, has a weight that indicates the strength of the connection between two neurons, and updating this weight must have linear and symmetric characteristics. Especially, a transistor-type device has a gate terminal, separating the processes of reading and updating the conductivity, used as a synaptic weight to prevent sneak path current issues during synaptic operations. In this study, we fabricate a top-gated flash memory device based on two-dimensional (2D) materials, MoS2 and graphene, as a channel and a floating gate, respectively, and Al2O3 and HfO2 to increase the tunneling efficiency. We demonstrate the linear weight updates and repeatable characteristics of applying negative/positive pulses, and also emulate spike timing-dependent plasticity (STDP), one of the learning rules in a spiking neural network (SNN).

Large Temperature-Independent Magnetoresistance without Gating Operation in Monolayer Graphene.

Temperature-independent magnetoresistance (TIMR) has been studied for applications in magnetic field sensors operating in wide temperature ranges. Graphene is considered as one of the best candidates for achieving nonsaturating and large TIMR through engineering disorders. Nevertheless, large TIMR has not been achieved in disordered graphene with intrinsic defects, such as chemical doping and atomic dislocations. In this work, by introducing extrinsic defects, we realize nonsaturating and large TIMR in monolayer graphene transferred on a BiFeO3 nanoisland array (G/BFO-NIA). Furthermore, the G/BFO-NIA device exhibits a significantly larger MR (∼250% under 9 T) than other materials without gating operation, demonstrating its application feasibility. It is shown that the large MR is a result of the coexistence of electrons and holes with almost the same density, and the observed TIMR originates from the temperature dependence of carrier transport in graphene and of the dielectric property of BFO-NIA.

Reversible switching mode change in Ta2O5-based resistive switching memory (ReRAM).

We report the complementary resistive switching (CRS) behaviors in a tantalum-oxide based resistive switching memory device that reversibly changes its switching mode between bipolar switching (BRS) and CRS in a single memory cell depending on the operation (compliance current) and fabrication (oxygen scavenger layer thickness) conditions. In addition, the origin of the switching mode transition was investigated through electrical and optical measurement, where the conductance is believed to be determined by two factors: formation of conductive filament and modulation of Schottky barrier. This result helps design a resistive switching device with desirable and stable switching behavior.

Phase Instability amid Dimensional Crossover in Artificial Oxide Crystal.

Artificial crystals synthesized by atomic-scale epitaxy provide the ability to control the dimensions of the quantum phases and associated phase transitions via precise thickness modulation. In particular, the reduction in dimensionality via quantized control of atomic layers is a powerful approach to revealing hidden electronic and magnetic phases. Here, we demonstrate a dimensionality-controlled and induced metal-insulator transition (MIT) in atomically designed superlattices by synthesizing a genuine two-dimensional (2D) SrRuO_{3} crystal with highly suppressed charge transfer. The tendency to ferromagnetically align the spins in an SrRuO_{3} layer diminishes in 2D as the interlayer exchange interaction vanishes, accompanying the 2D localization of electrons. Furthermore, electronic and magnetic instabilities in the two SrRuO_{3} unit cell layers induce a thermally driven MIT along with a metamagnetic transition.

Effect of Nb concentration on the spin-orbit coupling strength in Nb-doped SrTiO3 epitaxial thin films.

Several oxide materials have attracted much interest for the application in spintronic devices due to unusual properties originating from the strongly correlated orbital and spin degrees of freedom. One missing part in oxide spintronics is a good spin channel featured by strong spin-orbit coupling (SOC) which enables an efficient control of the electron's spin. We have systematically investigated the dependence of the SOC strength of Sr(Nb x Ti1-x)O3 thin films on Nb concentration (nNb = 2~20 at. %) as a deeper exploration of a recent finding of the strong SOC in a heavily Nb-doped SrTiO3 (Sr(Nb0.2Ti0.8)O3) epitaxial film. Apart from a finding of a proportionality of the SOC to nNb, we have observed an intriguing temperature dependence of the SOC strength and the anisotropic magnetoresistance (MR) in the intermediate nNb region. These phenomena are associated with the temperature dependence of Landé g-factor and the change of the band structure, which is consistent with the result of density functional theory (DFT) calculation.

Electrically Driven Reversible Phase Changes in Layered In2 Se3 Crystalline Film.

An unconventional phase-change memory (PCM) made of In2 Se3 , which utilizes reversible phase changes between a low-resistance crystalline ß phase and a high-resistance crystalline γ phase is reported for the first time. Using a PCM with a layered crystalline film exfoliated from In2 Se3 crystals on a graphene bottom electrode, it is shown that SET/RESET programmed states form via the formation/annihilation of periodic van der Waals' (vdW) gaps (i.e., virtual vacancy layers) in the stack of atomic layers and the concurrent reconfiguration of In and Se atoms across the layers. From density functional theory calculations, ß and γ phases, characterized by octahedral bonding with vdW gaps and tetrahedral bonding without vdW gaps, respectively, are shown to have energy bandgap value of 0.78 and 1.86 eV, consistent with a metal-to-insulator transition accompanying the ß-to-γ phase change. The monolithic In2 Se3 layered film reported here provides a novel means to achieving a PCM based on melting-free, low-entropy phase changes in contrast with the GeTe-Sb2 Te3 superlattice film adopted in interfacial phase-change memory.

Tuning electromagnetic properties of SrRuO3 epitaxial thin films via atomic control of cation vacancies.

Elemental defect in transition metal oxides is an important and intriguing subject that result in modifications in variety of physical properties including atomic and electronic structure, optical and magnetic properties. Understanding the formation of elemental vacancies and their influence on different physical properties is essential in studying the complex oxide thin films. In this study, we investigated the physical properties of epitaxial SrRuO3 thin films by systematically manipulating cation and/or oxygen vacancies, via changing the oxygen partial pressure (P(O2)) during the pulsed laser epitaxy (PLE) growth. Ru vacancies in the low-P(O2)-grown SrRuO3 thin films induce lattice expansion with the suppression of the ferromagnetic T C down to ~120 K. Sr vacancies also disturb the ferromagnetic ordering, even though Sr is not a magnetic element. Our results indicate that both A and B cation vacancies in an ABO3 perovskite can be systematically engineered via PLE, and the structural, electrical, and magnetic properties can be tailored accordingly.

Modulation of ABA Signaling by Altering VxGΦL Motif of PP2Cs in Oryza sativa.

The abscisic acid (ABA) signaling pathway is regulated by clade A type 2C protein phosphatases (PP2CAs) in plants. In the presence of ABA, PP2Cs release stress/ABA-activated protein kinases by binding to ABA-bound receptors (PYL/RCARs) for activation. Although the wedging tryptophan in PP2Cs is critical in the interaction with PYL/RCARs in Arabidopsis and rice, it remains elusive as to how other interface regions are involved in the interaction. Here, we report the identification of a conserved region on PP2Cs, termed the VxGΦL motif, which modulates the interaction with PYL/RCARs through its second and fourth residues. The effects of the second and fourth residues on the interaction of OsPP2C50 with several OsPYL/RCAR proteins were investigated by systematic mutagenesis. One OsPP2C50 mutant, VFGML ("FM") mutant, lowered the affinity to OsPYL/RCAR3 by ∼15-fold in comparison with the wild-type. Comparison of the crystal structures of wild-type OsPP2C50:ABA:OsPYL/RCAR3 with those composed of FM mutant revealed local conformational changes near the VxGΦL motif, further supported by hydrogen-deuterium exchange mass spectrometry. In rice protoplasts, ABA signaling was altered by mutations in the VxGΦL motif. Transgenic Arabidopsis plants overexpressing OsPP2C50 and OsPP2C50FM showed altered ABA sensitivity. Taken together, the VxGΦL motif of PP2Cs appears to modulate the affinity of PP2Cs with PYL/RCARs and thus likely to alter the ABA signaling, leading to the differential sensitivity to ABA in planta.

Large linear magnetoresistance in heavily-doped Nb:SrTiO3 epitaxial thin films.

Interaction between electrons has long been a focused topic in condensed-matter physics since it has led to the discoveries of astonishing phenomena, for example, high-Tc superconductivity and colossal magnetoresistance (CMR) in strongly-correlated materials. In the study of strongly-correlated perovskite oxides, Nb-doped SrTiO3 (Nb:SrTiO3) has been a workhorse not only as a conducting substrate, but also as a host possessing high carrier mobility. In this work, we report the observations of large linear magnetoresistance (LMR) and the metal-to-insulator transition (MIT) induced by magnetic field in heavily-doped Nb:STO (SrNb0.2Ti0.8O3) epitaxial thin films. These phenomena are associated with the interplay between the large classical MR due to high carrier mobility and the electronic localization effect due to strong spin-orbit coupling, implying that heavily Nb-doped Sr(Nb0.2Ti0.8)O3 is promising for the application in spintronic devices.

Free Fatty Acid Receptor 4 (GPR120) Stimulates Bone Formation and Suppresses Bone Resorption in the Presence of Elevated n-3 Fatty Acid Levels.

Free fatty acid receptor 4 (FFA4) has been reported to be a receptor for n-3 fatty acids (FAs). Although n-3 FAs are beneficial for bone health, a role of FFA4 in bone metabolism has been rarely investigated. We noted that FFA4 was more abundantly expressed in both mature osteoclasts and osteoblasts than their respective precursors and that it was activated by docosahexaenoic acid. FFA4 knockout (Ffar4(-/-)) and wild-type mice exhibited similar bone masses when fed a normal diet. Because fat-1 transgenic (fat-1(Tg+)) mice endogenously converting n-6 to n-3 FAs contain high n-3 FA levels, we crossed Ffar4(-/-) and fat-1(Tg+) mice over two generations to generate four genotypes of mice littermates: Ffar4(+/+);fat-1(Tg-), Ffar4(+/+);fat-1(Tg+), Ffar4(-/-);fat-1(Tg-), and Ffar4(-/-);fat-1(Tg+). Female and male littermates were included in ovariectomy- and high-fat diet-induced bone loss models, respectively. Female fat-1(Tg+) mice decreased bone loss after ovariectomy both by promoting osteoblastic bone formation and inhibiting osteoclastic bone resorption than their wild-type littermates, only when they had the Ffar4(+/+) background, but not the Ffar4(-/-) background. In a high-fat diet-fed model, male fat-1(Tg+) mice had higher bone mass resulting from stimulated bone formation and reduced bone resorption than their wild-type littermates, only when they had the Ffar4(+/+) background, but not the Ffar4(-/-) background. In vitro studies supported the role of FFA4 as n-3 FA receptor in bone metabolism. In conclusion, FFA4 is a dual-acting factor that increases osteoblastic bone formation and decreases osteoclastic bone resorption, suggesting that it may be an ideal target for modulating metabolic bone diseases.