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.

Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference.

The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.

Imaging Characterization of Non-Rheumatoid Retro-Odontoid Pseudotumors: Comparison with Atlantoaxial Manifestation of Rheumatoid Arthritis.

Backgroundand Objectives: To date, imaging characterization of non-rheumatic retro-odontoid pseudotumors (NRROPs) has been lacking; therefore, NRROPs have been confused with atlantoaxial joint involvement of rheumatoid arthritis (RA). It is important to differentiate these two disease because the treatment strategies may differ. The purpose of this study is to characterize imaging findings of NRROPs and compare them with those of RA. Material and Methods: From January 2015 to December 2019, 27 patients (14 women and 13 men) with NRROPs and 19 patients (15 women and 4 men) with RA were enrolled in this study. We evaluated various imaging findings, including atlantoaxial instability (AAI), and measured the maximum diameter of preodontoid and retro-odontoid spaces with magnetic resonance imaging (MRI) and computed tomography (CT). Results: Statistical significance was considered for p < 0.05. AAI was detected in eight patients with NRROPs and in all patients with RA (p < 0.0001). Seventeen patients with NRROPs and six patients with RA showed spinal cord compression (p = 0.047). Compressive myelopathy was observed in 14 patients with NRROPs and in 4 patients with RA (p = 0.048). Subaxial degeneration was observed in 25 patients with NRROPs and in 9 patients with RA (p = 0.001). Moreover, C2-3 disc abnormalities were observed in 11 patients with NRROPs and in 2 patients with RA (p = 0.02). Axial and longitudinal diameter of retro-odontoid soft tissue and preodontoid and retro-odontoid spaces showed significant differences between NRROP and RA patients (p < 0.0001). Furthermore, CT AAI measurements were differed significantly between NRROP and RA patients (p < 0.05). Conclusions: NRROPs showed prominent retro-odontoid soft tissue thickening, causing compressive myelopathy and a high frequency of subaxial and C2-3 degeneration without AAI.

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction.

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

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.

Tracheal Glomus Tumor: A Case Report with CT Imaging Features.

BACKGROUND AND OBJECTIVES: Glomus tumors are rare benign tumors. The majority of them affect the skin the most and are rarer in the trachea, where the glomus bodies may not be present. Only scarce reports of tracheal glomus tumors have been reported solely with case reports of relevant articles. MATERIALS AND METHODS: A 53-year-old man, with a free previous medial history, presented to our hospital with tracheal mass which was incidentally found. He did not complain of any specific symptoms associated with the tracheal tumor. The contrast-enhanced chest computed tomography (CT) revealed an avid enhancing nodular lesion, which is similar to blood vessels, in the trachea, 3 cm above the carina level without definite airway obstruction. RESULTS: Successful tracheal resection and end-to-end anastomosis were performed on the patients; therefore, the final post-operative pathologic findings revealed a benign tracheal glomus tumor. The follow-up CT scan four months after operation showed complete removal of the tumor. CONCLUSION: Tracheal glomus tumors, even rare entities, could be considered as a differential diagnosis if a highly enhancing mass appears on CT images.

Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing.

A charge trap device based on field-effect transistors (FET) is a promising candidate for artificial synapses because of its high reliability and mature fabrication technology. However, conventional MOSFET-based charge trap synapses require a strong stimulus for synaptic update because of their inefficient hot-carrier injection into the charge trapping layer, consequently causing a slow speed operation and large power consumption. Here, we propose a highly efficient charge trap synapse using III-V materials-based tunnel field-effect transistor (TFET). Our synaptic TFETs present superior subthreshold swing and improved charge trapping ability utilizing both carriers as charge trapping sources: hot holes created by impact ionization in the narrow bandgap InGaAs after being provided from the p+-source, and band-to-band tunneling hot electrons (BBHEs) generated at the abrupt p+n junctions in the TFETs. Thanks to these advances, our devices achieved outstanding efficiency in synaptic characteristics with a 5750 times faster synaptic update speed and 51 times lower sub-fJ/um2 energy consumption per single synaptic update in comparison to the MOSFET-based synapse. An artificial neural network (ANN) simulation also confirmed a high recognition accuracy of handwritten digits up to ∼90% in a multilayer perceptron neural network based on our synaptic devices.

Exploring dopant effects in stannic oxide nanoparticles for CO2 electro-reduction to formate.

The electrosynthesis of formate from CO2 can mitigate environmental issues while providing an economically valuable product. Although stannic oxide is a good catalytic material for formate production, a metallic phase is formed under high reduction overpotentials, reducing its activity. Here, using a fluorine-doped tin oxide catalyst, a high Faradaic efficiency for formate (95% at 100 mA cm-2) and a maximum partial current density of 330 mA cm-2 (at 400 mA cm-2) is achieved for the electroreduction of CO2. Furthermore, the formate selectivity (≈90%) is nearly constant over 7 days of operation at a current density of 100 mA cm-2. In-situ/operando spectroscopies reveal that the fluorine dopant plays a critical role in maintaining the high oxidation state of Sn, leading to enhanced durability at high current densities. First-principle calculation also suggests that the fluorine-doped tin oxide surface could provide a thermodynamically stable environment to form HCOO* intermediate than tin oxide surface. These findings suggest a simple and efficient approach for designing active and durable electrocatalysts for the electrosynthesis of formate from CO2.

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.

Bottom-Up Evolution of Diamond-Graphite Hybrid Two-Dimensional Nanostructure: Underlying Picture and Electrochemical Activity.

The diamond-graphite hybrid thin film with low-dimensional nanostructure (e.g., nitrogen-included ultrananocrystalline diamond (N-UNCD) or the alike), has been employed in many impactful breakthrough applications. However, the detailed picture behind the bottom-up evolution of such intriguing carbon nanostructure is far from clarified yet. Here, the authors clarify it, through the concerted efforts of microscopic, physical, and electrochemical analyses for a series of samples synthesized by hot-filament chemical vapor deposition using methane-hydrogen precursor gas, based on the hydrogen-dependent surface reconstruction of nanodiamond and on the substrate-temperature-dependent variation of the growth species (atomic hydrogen and methyl radical) concentration near substrate. The clarified picture provides insights for a drastic enhancement in the electrochemical activities of the hybrid thin film, concerning the detection of important biomolecule, that is, ascorbic acid, uric acid, and dopamine: their limits of detections are 490, 35, and 25 nm, respectively, which are among the best of the all-carbon thin film electrodes in the literature. This work also enables a simple and effective way of strongly enhancing AA detection.

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).

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.

3D Stackable Synaptic Transistor for 3D Integrated Artificial Neural Networks.

Although they have attracted enormous attention in recent years, software-based and two-dimensional hardware-based artificial neural networks (ANNs) may consume a great deal of power. Because there will be numerous data transmissions through a long interconnection for learning, power consumption in the interconnect will be an inevitable problem for low-power computing. Therefore, we suggest and report 3D stackable synaptic transistors for 3D ANNs, which would be the strongest candidate in future computing systems by minimizing power consumption in the interconnection. To overcome the problems of enormous power consumption, it might be necessary to introduce a 3D stackable ANN platform. With this structure, short vertical interconnection can be realized between the top and bottom devices, and the integration density can be significantly increased for integrating numerous neuromorphic devices. In this paper, we suggest and show the feasibility of monolithic 3D integration of synaptic devices using the channel layer transfer method through a wafer bonding technique. Using a low-temperature processible III-V and composite oxide (Al2O3/HfO2/Al2O3)-based weight storage layer, we successfully demonstrated synaptic transistors showing good linearity (αp/αd = 1.8/0.5), a high transconductance ratio (6300), and very good stability. High learning accuracy of 97% was obtained in the training of 1 million MNIST images based on the device characteristics.

K-means Data Clustering with Memristor Networks.

Memristor-based neuromorphic networks have been actively studied as a promising candidate to overcome the von-Neumann bottleneck in future computing applications. Several recent studies have demonstrated memristor network's capability to perform supervised as well as unsupervised learning, where features inherent in the input are identified and analyzed by comparing with features stored in the memristor network. However, even though in some cases the stored feature vectors can be normalized so that the winning neurons can be directly found by the (input) vector-(stored) vector dot-products, in many other cases, normalization of the feature vectors is not trivial or practically feasible, and calculation of the actual Euclidean distance between the input vector and the stored vector is required. Here we report experimental implementation of memristor crossbar hardware systems that can allow direct comparison of the Euclidean distances without normalizing the weights. The experimental system enables unsupervised K-means clustering algorithm through online learning, and produces high classification accuracy (93.3%) for the standard IRIS data set. The approaches and devices can be used in other unsupervised learning systems, and significantly broaden the range of problems a memristor-based network can solve.

Emulation of synaptic metaplasticity in memristors.

Recent studies have shown that nanoionic-based memristors can offer rich internal dynamics during ion movement that enables these solid-state devices to emulate various synaptic functions in biological systems naturally. The experimental observations can be explained within the 2nd-order memristor theoretical framework, which states that the device conductance (weight) can be determined by multiple internal state variables that can be modulated at different time scales and lead to different activity-dependent synaptic behaviors. Here, we show experimentally that not only the synaptic weight, but also synaptic plasticity (i.e. polarity and the rate of weight change) depends on the history of the input activities. This "plasticity of plasticity" resembles metaplasticity effects observed in biological systems, which have been found to facilitate neuron competition and stability. Specifically, we show that the memristor device may exhibit the same apparent weight (conductance) after experiencing different history of activities, but when subjected to additional, identical stimulation conditions, the device will however exhibit very different responses including the polarity and rate of weight (conductance) change. These findings serve to further our knowledge of fundamental physical mechanisms in memristors, and help advance adaptive artificial neuromorphic systems based on these emerging devices.

Infantile scrotal pyocele simulating missed testicular torsion on sonography.

We present the case of a neonate with idiopathic infantile pyocele. Scrotal sonography revealed a complex fluid collection within the left scrotal sac containing septations and a fluid-fluid level. The dependent region of the collection had moderate echogenicity, the slightly hypoechoic testis was not well defined, and the scrotal wall was thickened. Color Doppler sonography revealed mild hypervascularity in the thickened scrotal wall but no vascularity inside the testis. The sonographic findings suggested missed testicular torsion, but surgery revealed a pyocele, for which no source was identified. Radiologists should be aware that idiopathic infantile pyocele can mimic the Doppler sonographic findings in missed testicular torsion.