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under the different preparation conditions, the photoluminescence characteristic of porous silicon was different. This was a primary reason that caused many researches had the difference. This article used electrochemistry method to prepare porous silicon. The main research was that the influence of preparation conditions on the porous silicon's photoluminescence characteristic, the preparation conditions included the density of electrolyte、current density、corrosion time、lying time and so on。

This article gives two new and simple methods to treat porous silicon material, including acid treatment and cathode reduction treatment, to improve luminescence properties of porous silicon material. The results show that: the processing of cathode reduction can improve luminescence stability of porous silicon; the processing of acid treatment can effectively improve luminescence intensity of porous silicon. A conclusion can be drawn that the method of firstly processed porous silicon with cathode reduction treatment and secondly acid treatment has better effect to porous silicon luminescence intensity and stability than that of firstly acid treatment and secondly cathode reduction treatment.

The photoluminescence (PL) spectra at room temperature for the silicon-based samples doped by Nd and Ce by ion implantation are measured. The results show that all the samples possess blue-violet photoluminescence properties under the ultraviolet light excitation and its light emission is stable. The intensity of PL spectra is closely relative to the dose of implantation and to the temperature of thermal annealing.The feature and appearance of the samples was surveyed with atomic force microscopy(AFM).The results show that the surface grain size and coarse degree for sample have influence on its light emission efficiency.The light emission efficiency is higher when the surface grain size is symmetrical and average coarse degree is little for samples.The Rutherford Backscattering Spectrum(RBS) at room temperature for the samples was measured, the relationship between of luminescence properties of samples and morphology in implanted layer is investigated.The photoluminescence mechanism for our samples is also discussed.

Particle swarm optimization algorithm is a global optimization technique and a new technology base on swarm brainpower. This ideology comes from manpower anima and evolvement calculation theory. Its algorithm is simple for implement and excellent for application. Particle follow the one which is the best it found in the whole swarm to complete optimize. To solve the adjustable capability of fuzzy controlment and combine with the characteristic of nerve network, so fuzzy neural networks based on particle swarm optimization is designed in this paper. A nonlinear system is identified by the fussy neural networks. The distinguish process of fuzzy nerve network is confirming the precondition parameter and conclusion parameter. Simulation result indicates the great effect and potential in optimization of fuzzy nerve network. Base on this arithmetic’s speediness and availability, it can be use to practical field.