Diffraction behaviour of three-component fibonacci Ta/Al multilayer films

A class of quasiperiodic structure three-component Fibonacci (3CF) Ta/Al multilayer films is fabricated by dual-target magnetron sputtering. The microstructure of this film is investigated by transmission electron microscopy and electron and X-ray diffraction. Cross-section transmission electron microscopy demonstrates a well formed layer structure of 3CF Ta/Al superlattices. The electron-diffraction satellite spots, which can be indexed by three integers, correspond to the X-ray diffraction peaks in both position and intensity. The scattering vectors observed in electron and X-ray diffraction are in good agreement with the analytical treatment from the projection method

Transmission-electron microscopy study of the shape of buried InxGa1-xAs/GaAs quantum dots

High-resolution electron microscopy, on-zone bright-field imaging, and image simulation were used to investigate the shape of capped In0.06Ga0.4As/GaAs semiconductor quantum dots. Cross-section [110] high-resolution images suggest that the quantum dots are lens shaped, while the [001] on-zone bright-field images show a contrast that suggests a quantum dot morphology with four edges parallel to [100]. The image simulation, however, suggests that a spherical quantum dot can produce a square-shaped image. These observations lead to the conclusion that the quantum dots in buried In0.6Ga0.4As/GaAs semiconductor heterostructures are lens shaped

The Domination Number of Grids

In this paper, we conclude the calculation of the domination number of all $n\times m$ grid graphs. Indeed, we prove Chang's conjecture saying that for every $16\le n\le m$, $\gamma(G_{n,m})=\lfloor\frac{(n+2)(m+2)}{5}\rfloor -4$.Comment: 12 pages, 4 figure

Alloying, elemental enrichment, and interdiffusion during the growth of Ge(Si)/Si(001) quantum dots

Ge(Si)/Si(001) quantum dots produced by gas-source molecular beam epitaxy at 575 degreesC were investigated using energy-filtering transmission electron microscopy and x-ray energy dispersive spectrometry. Results show a nonuniform composition distribution in the quantum dots with the highest Ge content at the dot center. The average Ge content in the quantum dots is much higher than in the wetting layer. The quantum dot/substrate interface has been moved to the substrate side. A growth mechanism of the quantum dots is discussed based on the composition distribution and interfacial structures

Ion damage buildup and amorphization processes in GaAs-AlxGa1-xAs multilayers

The nature of ion damage buildup and amorphization in GaAs-AlxGa1-xAs multilayers at liquid-nitrogen temperature is investigated for a variety of compositions and structures using Rutherford backscattering-channeling and cross-sectional transmission electron microscopy techniques. In this multilayer system, damage accumulates preferentially in the GaAs layers; however, the presence of AlGaAs enhances the dynamic annealing process in adjacent GaAs regions and thus amorphization is retarded close to the GaAs-AlGaAs interfaces even when such regions suffer maximum collisional displacements. This dynamic annealing in AlGaAs and at GaAs-AlGaAs interfaces is more efficient with increasing Al content; however, the dynamic annealing process is not perfect and an amorphous phase may be formed at the interface above a critical defect level or ion dose. Once an amorphous phase is nucleated, amorphization proceeds rapidly into the adjacent AlGaAs. This is explained in terms of the interplay between defect migration and defect trapping at an amorphous-crystalline or GaAs-AlGaAs interface. In addition, enhanced recrystallization of the amorphous GaAs at the interface may occur during heating if an amorphous phase is not formed in the adjacent AlGaAs layer. This is most likely the result of mobile defects injected from the AlGaAs layer during heating. (C) 1996 American Institute of Physics

Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimized implementation is provided in Python