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EN
This work presents an analysis of the influence of SiO2 dielectric coverage of a Si substrate on the solar-cell efficiency of Si thin layers obtained by epitaxial lateral overgrowth (ELO). The layers were obtained by liquid phase epitaxy (LPE). All experiments were carried out under the following conditions: initial temperature of growth: 1193 K; temperature difference ^T = 60 K; ambient gas: Ar; metallic solvent: Sn+Al; cooling rates: 0.5 K/min and 1 K/min. To compare the influence of the interior reflectivity of photons, we used two types of dielectric masks in a shape of a grid etched in SiO2 along the <110> and <112> directions on a p+ boron-doped (111) silicon substrate, where silicon dioxide covered 70 % and 80 % of the silicon surface, respectively. The results obtained in this work depict the correlation between the interior efficiency and percentage of SiO2 coverage of the substrate of the ELO solar cells.
2
Content available remote Finite element method simulation of interface evolution during epitaxial growth
EN
Epitaxial Lateral Overgrowth (ELO) is a method of epitaxial growth on a partially masked substrate. It can be a promising method for photovoltaic applications due to a possibility of producing thin and high quality silicon substrates. Since the mask prevents propagation of the substrate dislocations to the laterally overgrown parts of the ELO layer they are characterized by a lower dislocation density than the substrate. It means that it is possible to fabricate good quality solar cells on a poor quality Si substrate. The main goal of the research is to obtain a higher growth rate in the lateral direction than in the direction normal to the substrate. The epilayer growth kinetics depends on many technological factors, basically the growth temperature, the cooling rate, the solvent and the mask filling factor. For this reason the best way to achieve the goal is a computational analysis of the epitaxial layer growth process. This work presents a two-dimensional computational study of such a process of growth for different technological conditions. The computational model is based on the assumption of pure diffusion control growth.
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