Si(100) Surface Reordering upon Ga Adsorption

Abstract

In a study of room temperature Ga adsorption on clean, cleaved, 2×1 reconstructed Si (111) surface /1/ made earlier, it was shown that 1/3 of a monolayer of Ga (that is 2.9 × 1014 Ga atoms per cm2) induces a √3 × √3 R 30° reconstruction which replaces the 2×1, removes the Si surface dangling bond peak and decreases both the ionization energy and the work function by roughly the same amount of 0.4 eV. This was explained by the covalent bonding of each trivalent Ga atom with three Si surface atoms in a site of ternary symmetry which has been shown to be the one on top of the second layer Si atom /2/. Further Ga coverage brings a statistically uniform, disordered and non metallic layer over a 1×1 silicon surface periodicity at 1 monolayer, followed by the formation of metallic Ga islands. In the present paper the effects of Ga adsorption on clean 2×1 reconstructed Si(100) surfaces are compared to those above, knowing that the trivalent Ga meets this time a surface which does not display sites with ternary symmetry, being ideal or 2×1 reconstructed. The actual structure of the clean (100) face of Si is recognized /3,4,5/ as formed essentially of tilted dimers and complementary local structures associated with the presence of more or less ordered surface defects (steps, single and grouped vacancies and adatoms). The electronic properties of the (100) surface are known and roughness effects established /6/. In the following, using low energy electron diffraction (LEED), Auger electron spectroscopy (AES) and photoemission yield spectroscopy (PYS), it will be shown how Ga adsorption in its early stages can transform the Si(100) reconstruction and roughness. To our knowledge, no otner work has been done on the Ga-Si(100) system and comparisons will be made with Ga-Si(111) /1/ and In-Si(100) /7/.KeywordsAuger Electron SpectrosAuger SignalAdsorption StageAuger Electron SpecAuger Electron SpectrosThese 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.