Nonvolatile memories using deep traps formed in HfO₂ by Nb ion implantation

We report nonvolatile memories (NVMs) based on deep-energy trap levels formed in HfO₂ by metal ion implantation. A comparison of Nb- and Ta-implanted samples shows that suitable charge-trapping centers are formed in Nb-implanted samples, but not in Ta-implanted samples. This is consistent with density-functional theory calculations which predict that only Nb will form deep-energy levels in the bandgap of HfO₂. Photocurrent spectroscopy exhibits characteristics consistent with one of the trap levels predicted in these calculations. Nb-implanted samples showing memory windows in capacitance–voltage (V) curves always exhibit current (I) peaks in I–V curves, indicating that NVM effects result from deep traps in HfO₂. In contrast, Ta-implanted samples show dielectric breakdowns during the I–V sweeps between 5 and 11 V, consistent with the fact that no trap levels are present. For a sample implanted with a fluence of 10¹³Nb cm⁻², the charge losses after 10⁴ s are ∼9.8 and ∼25.5% at room temperature (RT) and 85°C, respectively, and the expected charge loss after 10 years is ∼34% at RT, very promising for commercial NVMs

Dipole-Allowed Direct Band Gap Silicon Superlattices

Silicon is the most popular material used in electronic devices. However, its poor optical properties owing to its indirect band gap nature limit its usage in optoelectronic devices. Here we present the discovery of super-stable pure-silicon superlattice structures that can serve as promising materials for solar cell applications and can lead to the realization of pure Si-based optoelectronic devices. The structures are almost identical to that of bulk Si except that defective layers are intercalated in the diamond lattice. The superlattices exhibit dipole-allowed direct band gaps as well as indirect band gaps, providing ideal conditions for the investigation of a direct-to-indirect band gap transition. The transition can be understood in terms of a novel conduction band originating from defective layers, an overlap between the valence- and conduction-band edge states at the interface layers, and zone folding with quantum confinement effects on the conduction band of non-defective bulk-like Si. The fact that almost all structural portions of the superlattices originate from bulk Si warrants their stability and good lattice matching with bulk Si. Through first-principles molecular dynamics simulations, we confirmed their thermal stability and propose a possible method to synthesize the defective layer through wafer bonding

What is Needed the Most in MT-Supported Paper Writing

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure

Ultraviolet photodepletion spectroscopy of dibenzo-18-crown-6-ether complexes with alkali metal cations

Ultraviolet photodepletion spectra of dibenzo-18-crown-6-ether complexes with alkali metal cations (M+-DB18C6, M = Cs, Rb, K, Na, and Li) were obtained in the gas phase using electrospray ionization quadrupole ion-trap reflectron time-of-flight mass spectrometry. The spectra exhibited a few distinct absorption bands in the wavenumber region of 35450−37800 cm^(−1). The lowest-energy band was tentatively assigned to be the origin of the S_0-S_1 transition, and the second band to a vibronic transition arising from the “benzene breathing” mode in conjunction with symmetric or asymmetric stretching vibration of the bonds between the metal cation and the oxygen atoms in DB18C6. The red shifts of the origin bands were observed in the spectra as the size of the metal cation in M^+-DB18C6 increased from Li^+ to Cs^+. We suggested that these red shifts arose mainly from the decrease in the binding energies of larger-sized metal cations to DB18C6 at the electronic ground state. These size effects of the metal cations on the geometric and electronic structures, and the binding properties of the complexes at the S_0 and S_1 states were further elucidated by theoretical calculations using density functional and time-dependent density functional theories

An Efficient Building Evacuation Algorithm in Congested Networks

This paper proposes a new network model for the building evacuation problem considering congestion levels and provides a mixed integer linear programming (MILP) model and an efficient heuristic algorithm solving the problem. Constructing an optimization model with several congestion levels, we introduce a new network called the multi-class time-expanded (MCTE) network having several exclusive arcs connecting the same tail and head nodes. The MCTE networks make both the MILP model and the heuristic algorithm reflect a realistic situation in congested networks. Considering MCTE networks makes the problem difficult to solve, which motivates us to develop an efficient heuristic algorithm. We test our heuristic algorithm using several real-world networks such as a multiplex cinema, a subway station, and a large-size complex shopping mall in addition to an artificial network for clear comparison between the proposed algorithm and the MILP approaches. The results indicate that the proposed algorithm runs fast and produces a near-optimal solution compared with those from MILP models with a commercial solver.11Ysciescopu

Implicit Inverse Force Identification Method of Acoustic Liquid-structure Interaction Finite Element Model

The two-field vibroacoustic finite-element (FE) model requires a relatively large number of degrees of freedom compared to the monophysics model, and the conventional force identification method for structural vibration can be adjusted for multiphysics problems. In this study, an effective inverse force identification method for an FE vibroacoustic interaction model of an interior fluid-structure system was proposed. The method consists of: (1) implicit inverse force identification based on the Newmark-$\beta$ time integration algorithm for stability and efficiency, (2) second-order ordinary differential formulation by avoiding the state-space form causing large degrees of freedom, (3) projection-based multiphysics reduced-order modeling for further reduction of degrees of freedom, and (4) Tikhonov regularization to alleviate the measurement noise. The proposed method can accurately identify the unmeasured applied forces on the in situ application and concurrently reconstruct the response fields. The accuracy, stability, and computational efficiency of the proposed method were evaluated using numerical models and an experimental testbed. A comparative study with the augmented Kalman filter method was performed to evaluate its relative performance.Comment: 31 Pages, 20 Figures, 5 Table