On possible spin injection at non-ideal Schottky contacts

The role of the tunneling mechanisms in metal-disordered layer-semiconductor structure under spin injection at the interface is investigated. The non-ideal metal-semiconductor structure as prepared by ionized cluster beam deposition is considered, and it is shown that the depletion region of the semiconductor can be tailored to include a suitably heavily doped region near the interface. The tunneling is described within a simplified model in which the expression for the interface resistance of the metal-disordered layer-semiconductor structure is obtained. It is argued that in the case of ionized cluster beam deposited non-ideal Schottky structure a significant spin injection is achieved.     

Real time ellipsometry for monitoring plasma-assisted epitaxial growth of GaN

GaN is grown on Si-face 4H-SiC(0 0 0 1) substrates using remote plasma-assisted methods including metalorganic chemical vapour deposition (RP-MOCVD) and molecular beam epitaxy (MBE). Real time spectroscopic ellipsometry is used for monitoring all the steps of substrate pre-treatments and the heteroepitaxial growth of GaN on SiC. Our characterization emphasis is on understanding the nucleation mechanism and the GaN growth mode, which depend on the SiC surface preparation.     

The ALTO project at IPN-Orsay

The availability of both a tandem deuteron beam and a linac electron beam, the latter converted into Bremsstrahlung, at the new ALTO facility at IPN-Orsay offers a unique opportunity to compare the performance of a laser ion guide under different regimes. The ALTO accelerator has delivered its first electron beam at the end of 2005 and a design for a gas-cell prototype is being studied.     

Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components