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.

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.

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.

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.

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.

Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery.

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.

Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model.

Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up.

Fractionally δ-doped oxide superlattices for higher carrier mobilities.

A two-dimensional (2D) electron gas system in an oxide heterostructure serves as an important playground for novel phenomena. Here, we show that, by using fractional δ-doping to control the interface's composition in La(x)Sr(1-x)TiO(3)/SrTiO(3) artificial oxide superlattices, the filling-controlled 2D insulator-metal transition can be realized. The atomic-scale control of d-electron band filling, which in turn contributes to the tuning of effective mass and density of the charge carriers, is found to be a fascinating route to substantially enhanced carrier mobilities.

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.

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.

Dysadherin expression promotes the motility and survival of human breast cancer cells by AKT activation.

High dysadherin expression has been recognized as a biological predictor of metastasis and poor prognosis for many different cancer types; however, the molecular mechanisms of how dysadherin affects cancer progression are still poorly understood. In this study, we examined whether AKT signaling could link dysadherin expression with downstream events that promote the metastatic potential of human breast cancer cells. Immunohistochemical analysis of breast cancer tissues showed that dysadherin expression was highly associated with elevated expression of phospho-AKT. The introduction of dysadherin cDNA into BT-474, MCF-7 and T-47D breast cancer cell lines enhanced their levels of AKT phosphorylation, while knockdown of dysadherin in MDA-MB-231 and Hs578T breast cancer cell lines suppressed AKT phosphorylation. Treatment with the AKT inhibitor triciribine suppressed dysadherin-mediated pro-metastatic effects, including epithelial-mesenchymal transition, cell motility and drug resistance. These findings suggest that dysadherin might contribute to breast cancer progression through AKT activation.

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.

Renal ischemia-reperfusion injury causes intercalated cell-specific disruption of occludin in the collecting duct.

Renal ischemic events open tight junctions and disrupt epithelial polarity. The purpose of this study was to examine the effects of ischemia-reperfusion (IR) injury on expression and distribution of the tight junction proteins, occludin and ZO-1, in the rat kidney. IR injury was induced by clamping both renal pedicles for 30 min and animals were killed at 6 h after the reperfusion. IR injury decreased blood bicarbonate level, but did not persistently alter pH, Na(+), K(+), or Cl(-). In control kidneys, occludin immunoreactivity was intense in the tight junctions in the thick ascending limb, distal convoluted tubule, and collecting duct, moderate in the thin limbs of the loop of Henle, and was not detected in the proximal tubule, glomerulus, and blood vessels. ZO-1 was expressed in the same sites in which occludin was expressed, and additionally was also expressed in the proximal tubule, glomerulus, and vascular endothelial cells. IR kidneys exhibited damaged renal tubular epithelial cells in both proximal tubule and collecting duct segments in the outer medulla. In the collecting duct, the response of intercalated cells and principal cells differed. Following IR injury, intercalated cells, but not principal cells, lost their normal epithelial polarity and were frequently extruded into the tubule lumen. Occludin, instead of being localized to tight junctions, was localized diffusely in the cytoplasm in intercalated cells of IR kidneys. Principal cells, in contrast, were not detectably affected and neither occludin nor ZO-1 expression were altered in response to IR injury. The normal localization of ZO-1 expression to tight junction sites in both the proximal tubule and collecting duct was altered in response to IR, and, instead, ZO-1 expression was present diffusely in the cytoplasm. IR injury did not alter detectably either occludin or ZO-1 localization to the tight junction of the thick ascending limb cells. The abundance of total occludin protein by immunoblot analysis was not changed with IR injury. These results demonstrate that renal IR injury causes tight junction disruptions in both the proximal tubule and the collecting duct, and that altered distribution of the tight junction protein, occludin, may play a critical role in the collecting duct dysfunction which IR induces.

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.

Expression of ammonia transporter family members, Rh B glycoprotein and Rh C glycoprotein, in the developing rat kidney.

Ammonia metabolism is a primary component of acid-base homeostasis but is incompletely developed at time of birth. Rh B glycoprotein (Rhbg) and Rh C glycoprotein (Rhcg) are recently recognized ammonia transporter family members expressed in the mammalian kidney. This study's purpose was to establish the expression and localization of Rhbg and Rhcg during kidney development. We examined kidneys from fetal days 16 (E16), 18 (E18), and 20 (E20), and from the first 21 days of postnatal development. Rhbg was expressed initially at E18, with expression only in the connecting tubule (CNT); at E20, Rhbg was expressed in both the CNT and the medullary collecting duct (MCD). In contrast, Rhcg was first expressed at E16 with basal expression in the ureteric bud; at E18, it was expressed in a subset of CNT cells with an apical pattern, followed by apical and basolateral expression in the MCD at E20. In the cortex, Rhbg and Rhcg expression increased in the CNT before expression in the cortical collecting duct during fetal development. In the MCD, both Rhbg and Rhcg expression was initially in cells in the papillary tip, with gradual removal from the tip during the late fetal period and transition during the early neonatal period to an adult pattern with predominant expression in the outer MCD and only rare expression in cells in the initial inner MCD. Double-labeling with intercalated cell-specific markers identified that Rhbg and Rhcg were expressed initially in CNT cells, CNT A-type intercalated cells and non-A, non-B intercalated cells, and in MCD A-type intercalated cells. We conclude that expression of Rhbg and Rhcg parallels intercalated cell development and that immature Rhbg and Rhcg expression at birth contributes to incomplete ammonia excretion capacity.

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.

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.

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).