Electrical properties of MIS structures with silicon nanoclusters
The theoretical and experimental investigations of electrical properties of the Al-SiO2- -SiO ( ncs Si − 2-Si structures grown using high temperature annealing SiOx, x < 2, have been carried out. It has been experimentally found that the Al-SiO2- ( ) ncs Si − -SiO2-Si structures with the tunnel dielectric layer revealed the effect of dynamic memory. Electric properties and parameters of the interface states located between and SiO ncs Si− 2 were studied in detail by measuring of current-voltage, capacitance-voltage, and thermally stimulated current characteristics.
Electrical properties of semiconductor structures with Si nanoclusters in SiO2 grown by high temperature annealing technology of SiO X layer, X<2
The theoretical and experimental investigations of electrical properties of the SiO2/Si-ncs/SiO2/Si structures grown by high temperature annealing SiOX, X<2, have been carried out. The influence of Si cluster growth conditions on frequency dependences of V C  characteristics, static and dynamic conductance of investigated structures has been clearly observed. As a result of theoretical modeling, V C  dependences have been calculated. The experimentally obtained negative constituent of differential capacitance has been qualitatively described. It has been experimentally found that the SiO2/Si-ncs/SiO2/Si structures with the tunnel dielectric layer revealed the effect of memorizing.
Creating the Virtual Universe
Simulation software is important to our understanding of the universe. The intrinsic multiphysics aspects are spiced with a range of temporal scales and spatial scales, both of which cover more digits than are available in the standard hardware. This, together with the intrinsic chaotic nature of many physical processes, poses quite a challenge. To meet this challenge, researchers developed the Astronomical Multipurpose Environment (AMUSE). Instead of writing a suite of multiphysics solvers from scratch, AMUSE's developers coupled existing solvers for each physical ingredient. The result is a highly inhomogeneous collection of dedicated solvers with a homogeneous protocol that scales to supercomputers.