Photoluminescent Nanosilicon Systems

Abstract

The porous silicon (por-Si) is one of the brightest example of nanosilicon systems. Non stability of the luminescent properties of porous silicon is the main reason that prevents por-Si applications for manufacturing efficient light emitting devices. For the solution of this problem the great deal of investigations was focused around different kinds of surface treatments.It was observed that intensity of the visible photoluminescence (PL) of the samples rises after its exposure in the air at the room temperature because of oxygen impregnation to the deep layer of por-Si and formation of SiOx clusters on the surface of Si-wires skeleton. The usage of second ion mass spectrometry (SIMS) allows to conclude that porous Si storage in the air ambient leads to the essentially nonuniformity distribution oxygen and hydroxyl groups after removing of surface layer thickness.At the same time when the por-Si samples were subjected to pulse rapid thermal annealing (PTA) in an argon environment (the treatment temperature was 1100 K for a period 30 sec.) considerable transformation of spectral bands was observed: the integral spectra of por-Si shows two intensive bands at 720 and 540 nm.As well silicon carbide films were deposited on the surface of por-Si samples by ionplasma sputtering of a SiC target in argon-hydrogen vapor atmosphere. Deposition of thin (~80 nm) SiC films on the por-Si surface leads to decreasing of PL intensity in long-wave spectral range. Besides this, the new band of blue light emission appears. These changes in PL spectra of porous Si are explained by SiC clusters formation on the Si-wires skeleton of por-Si.The spectral changes peculiarities of nanosilicon system depend from manufacturing methods and porosity of por-Si. Nevertheless, the system has the stable PL characteristics over the time.KeywordsPorous SiliconRapid Thermal AnnealingSingle Crystal SiliconQuantum WirePrincipal AngleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.