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We report the first detection of Y280‐lineage H9N2 avian influenza viruses in live bird markets in Korea during July 2020. The viruses were isolated from domestic ducks and chickens traded in three markets in two different provinces, indicating dispersal of the newly introduced viruses. Complete genome sequencing and comparative phylogenetic analyses of all eight gene segments of the viruses showed high nucleotide homology to a Y280‐lineage H9N2 avian influenza virus isolated in a chicken farm in China, which belongs to one of the most prevalent H9N2 genotypes in China. Increasing human cases of the same genotype H9N2 infection in China and the mammalian specific markers present in the viruses isolated suggest potential implications for public health. [ABSTRACT FROM AUTHOR]

As the computing paradigm has shifted toward edge computing, improving the security of edge devices is attracting significant attention. However, because edge devices have limited resources in terms of power and area, it is difficult to apply a conventional cryptography system to protect them. On the other hand, as a simple security application, a physical unclonable function (PUF) can be implemented without power and area problems because it provides a security key by utilizing process variations without additional external circuits. Ferroelectric tunnel junctions (FTJs) are 2-terminal devices that store information by changing the resistance of a ferroelectric material, where the resistance is determined by the polarization states of the ferroelectric domains. Because polycrystalline ferroelectric materials have a multi-domain nature, domain variation can also be used as a randomness source to induce cell-to-cell variations along with process variations. In this paper, we demonstrate PUF operations of a low-power, small area 16 × 16 hafnium oxide (pure-HfOx)-based FTJ array using certain metrics. It is clear that the proposed array consisting of scaled FTJs has adequate randomness for security applications such that the array-level PUF operations are robust against model-based machine learning attacks. [ABSTRACT FROM AUTHOR]

In recent days, more hardware-driven artificial intelligence system capable of brain-like low-energy consumption is gaining ever-increasing interest. The hardware-driven property lies in the low-power synaptic device and its array along with the area and energy-efficient neuron circuits. In this work, a spiking neural network (SNN) based on analog synaptic device of resistive-switching random access memory (RRAM) is constructed from the experimentally fabricated devices. Furthermore, the capability of the designed SNN hardware for sequential tasks through an optimal reinforcement learning (RL) algorithm is demonstrated. More specifically, the Rush Hour game is conducted as an example of applications for the sequential task for which an SNN architecture is plausibly suited. The rule of the game is simple but has not been demonstrated by a hardware-oriented artificial neural network (ANN) yet, and in this work, it is reported that the analog RRAM synaptic devices in the cross-point array architecture successfully solve the problem via the RL algorithm. [ABSTRACT FROM AUTHOR]

As interest in artificial intelligence (AI) and relevant hardware technologies has been developed rapidly, algorithms and network structures have become significantly complicated, causing serious power consumption issues because an enormous amount of computation is required. Neuromorphic computing, a hardware AI technology with memory devices, has emerged to solve this problem. For this application, multilevel operations of synaptic devices are important to imitate floating point weight values in software AI technologies. Furthermore, weight transfer methods to desired weight targets must be arranged for off-chip training. From this point of view, we fabricate 32 × 32 memristor crossbar array and verify the 3-bit multilevel operations. The programming accuracy is verified for 3-bit quantized levels by applying a reset-voltage-control programming scheme to the fabricated TiOx/Al2O3-based memristor array. After that, a synapse composed of two differential memristors and a fully-connected neural network for modified national institute of standards and technology (MNIST) pattern recognition are constructed. The trained weights are post-training quantized in consideration of the 3-bit characteristics of the memristor. Finally, the effect of programming error on classification accuracy is verified based on the measured data, and we obtained 98.12% classification accuracy for MNIST data with the programming accuracy of 1.79% root-mean-square-error. These results imply that the proposed reset-voltage-control programming scheme can be utilized for a precise tuning, and expected to contribute for the development of a neuromorphic system capable of highly precise weight transfer. [ABSTRACT FROM AUTHOR]

Experiments are conducted to compare the resistive switching characteristics for several samples with different amounts of Ag deposition in TiN/Ag/SiNx/TiN conductive bridging random access memory (CBRAM). The compliance current in TiN/Ag/SiNx/TiN CBRAM determines the volatile/non-volatile memory operation as the current level controls the strength of the filament made of Ag. The transient measurement showed that the effective thickness of Ag source layer in the TiN/Ag/SiNx/TiN controls the supply of the Ag atoms into the insulating layer, affecting the strength of the conductive bridge. The mechanism for the switching characteristics and the volatility trend with the amount of Ag deposition is closely investigated using transmission electron microscopy and scanning electron microscopy images. The device shows the conductance potentiation by a voltage pulse train under 1 µA current level, and the higher potentiation rate is observed in the CBRAM with thick Ag source layer. [ABSTRACT FROM AUTHOR]

Although insertion of a thin insulating layer between metal electrodes and a semiconducting channel is an effective way to improve device performance, the exact reason for improvement in performance is not elucidated. Herein, the role of an Al2O3 interlayer sandwiched between Al metal electrodes and an amorphous indium–gallium–zinc–oxide semiconducting channel is systematically investigated. The Al2O3 interlayer results in not only a good transistor performance with increased on current but also improved gate bias stress stability. The improvement is primarily attributed to a doping effect and mitigation of interface defects. Energy‐band diagrams, experimentally obtained from temperature‐variable electrical characterization and electrostatic force microscopy, validate the channel doping effect, which increase the tunneling probability of the electron charge carriers via a reduction of the Schottky barrier width. A comprehensive study on the influence of various processing parameters, including Al2O3 thickness, post‐annealing treatment conditions, and types of electrodes, on the transistor device is also performed. This approach guides the practical implementation of stable sol–gel oxide‐based thin‐film transistors and promotes integrated circuitry applications. [ABSTRACT FROM AUTHOR]

We fabricated a silicon based nano-wedge resistive switching memory device with the stack of Ti/HfOx/p+-Si. By using 25% tetra-methyl-ammonium hydroxide (TMAH) aqueous solution, the anisotropic wet etching process is carried out to minimize the tip structure of the silicon bottom electrode to a width of 4 nm, and the structure was validated through TEM analysis. Due to the minimized device area, low read current levels (<1 μA) were obtained in the nano-wedge RRAM while the opposites were measured in large size RRAM devices. In addition, the fabricated nano-wedge RRAM exhibited low power consumption during the DC switching process. Additionally, pulse measurement and retention tests were performed to demonstrate the synaptic behaviors of long-term potentiation and depression. Software neural network simulation was followed to test the feasibility of nano-wedge RRAM's array implementation. These results demonstrate the fabricated nano-wedge RRAM devices' potential usage as a synaptic device in neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

In this article, we report a method for recovering a resistive-switching random access memory (RRAM) from the reset breakdown and enhancing the endurance characteristics. A SiNx-based RRAM device has been fabricated and its switching characteristics are analyzed with a particular interest in reset breakdown. It has been found that the SiNx RRAM in permanent reset breakdown can be revived to high-resistance state (HRS) by thermal recovery, a low-temperature annealing method. The memory window could be widened even with enhanced reset stability and conductance distribution. Temperature-dependent conductance change has been measured in order to figure out the substantial component of conductive filament and the mechanism of thermal recovery. The experimental evidences show that the reset breakdown process is the result of unwanted Si dangling bond (Si-DB) formation. By thermal recovery, Ni filament could be ruptured without applying a high electric field which induces negative set. [ABSTRACT FROM AUTHOR]