Lattice disorder in silicon induced by 2.0 MeV Cu+ irradiation

The effect of 2.0 MeV Cu⁺ irradiation on Si(100) crystal has been studied by the Rutherford backscattering/channeling technique. Analysis of the lattice disorder distribution has been performed under 100 direction of tilting off from the target normal: 7°, 30°, and 45° as well as different doses. The lattice disorder distributions in Si(100) have been compared with TRIM'89 simulation. The results show that the lattice disorder distributions in Si(100) under different irradiation angles seem to be in good agreement with TRIM'89 simulation. When the dose increases up to 8.7×10¹⁴ ions/cm², the defect concentration increases leading to the formation of an amorphous layer.     

A study of the density of gap states in amorphous semiconductors from thermostimulated conductivity spectra

A theoretical analysis of thermostimulated conductivity spectra _TSC(T) has been applied to determine the density of gap states g(E) in a-Si: H and a-Si: H/a-SiN _x : H multilayer structures. The results for g(E) are consistent with the results deduced from Fritzsche's analytical approach as well as other methods. A comparison has been made between the two different analytical approaches for _TSC(T). We discuss the relationship between the energy of maximum thermostimulated current emission E _m and quasi-Fermi level E _q. We demonstrate that E _q could be a better parameter than E _m in the general theoretical treatment of thermostimulated conductivity.     

Improved results on the robustness of stochastic approximation algorithms

This paper is a continuation of the research carried out in [1]–[2], where the robustness analysis for stochastic approximation algorithms is given for two cases: 1. The regression function and the Liapunov function are not zero at the sought-forx ⁰; 2. lim sup _n a131_{n i=1} ⁿ _i+1 is not zero, where { _i } are the measurement errors and {a _n} are the weighting coefficients in the algorithm. Allowing these deviations from zero to occur simultaneously but to remain small, this paper shows that the estimation error is still small even for a class of measurement errors more general than that considered in [2].This project is supported by the National Natural Science Foundation of China.     

A path following algorithm for a class of convex programming problems

We present a primal-dual path following interior algorithm for a class of linearly constrained convex programming problems with non-negative decision variables. We introduce the definition of a Scaled Lipschitz Condition and show that if the objective function satisfies the Scaled Lipschitz Condition then, at each iteration, our algorithm reduces the duality gap by at least a factor of (1–/n), where is positive and depends on the curvature of the objective function, by means of solving a system of linear equations which requires no more than O(n³) arithmetic operations. The class of functions having the Scaled Lipschitz Condition includes linear, convex quadratic and entropy functions.