Ge nanodots (NDs) for nonvolatile memories (NVMs) have been self-assembled at room temperature (RT) by ion beam sputtering deposition of ultrasmall amount Ge (<72 ML) on SiO2 without postannealing. High-resolution transmission electron microscopy demonstrates the existence of well-defined Ge ND layers with respect to the SiO2∕Si interface. As Ge amount increases, the size of NDs increases, while their density decreases. A possible mechanism is proposed to explain the formation of Ge NDs at RT based on simple model calculations. The memory window that is estimated by capacitance-voltage hysteresis increases up to 18.7V with increasing Ge amount up to 54 ML. The program speed is enhanced by increasing Ge amount and the charge-loss speed in the programed state is slower for larger Ge amount. These NVM properties are very promising in view of device application.

1-5 period multilayers of Ge nanodots (NDs) for nonvolatile memories have been self-assembled by ion beam sputtering deposition of an ultra-small amount of Ge between SiO(2) layers at room temperature without post-annealing. High-resolution transmission electron microscopy demonstrates the existence of Ge ND layers well defined with respect to the SiO(2)/Si interface. The memory window that is estimated by capacitance-voltage hysteresis is proportional to the period, and finally reaches a plateau of about 11 V asymptotically over three periods. The program speed is enhanced over the full pulse-time range by increasing bias voltage or period. The charge-loss speed in the programmed state is slower in the samples with larger period. These memory properties are discussed based on possible physical mechanisms.

Amorphous InGaZnO (IGZO) thin films were grown using RF sputtering deposition at room temperature and their corresponding dielectric functions were measured. In order to reduce defects and increase carrier concentrations, we examined the effect of forming gas annealing and ion implantation. The band gap energy increased with increasing forming gas annealing temperature. We implanted the IGZO thin films with F À ions in order to decrease oxygen vacancies. For comparison, we also implanted InO À ions. Transmission electron microscopy showed that the amorphous phase undergoes transformation to a nanocrystalline phase due to annealing. We also observed InGaZnO 4 nanocrystals having an In-(Ga/Zn) superlattice structure. As the annealing temperature increased, the optical gap energy increased due to crystallization. After annealing, we observed an oxygen-vacancy-related 1.9 eV peak for both unimplanted and InO-implanted samples. However, F À ion implantation substantially reduced the amplitude of the 1.9 eV peak, which disappeared completely at a F fluence of 5 Â 10 15 cm À2 . We observed other defect-related peaks at 3.6 and 4.2 eV after annealing, which also disappeared after F implantation.

Bridge damage is a case in which physical forces act on the inside and outside of the bridge and maintain it in an unstable state, such as cracks, breakage, deformation, and erosion. As concrete is a brittle material, it is easily broken by external impact and can lead to serious accidents due to loss of function, so periodic management is essential. In this paper, we propose a crack management system using the embedded device for drone mounting. The embedded device for drone mounting is connected to a distance sensor, GPS module, and a camera to photograph the bridge in real time and uses a machine learning algorithm to find cracks in the bridge. When the embedded device finds a crack with a camera, it sends JSON formatted crack detection data including GPS position, timestamp, base64 encoded image data, and additional information to the crack management server over MQTT. We also propose a crack management algorithm and implemented a crack management server using a proposed algorithm. A proposed crack management server is implemented by the python web framework flask and a tensorflow machine learning library, and saves data with a mariaDB database server. The proposed system can enable fast and efficient management by using a real-time analysis method rather than a method of analyzing filmed video.

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