Spectroscopic ellipsometric properties of annealed Mn1.95Co0.77Ni0.28O4 thin films

Mn-Co-Ni-O thin films have important application values, but their spectroscopic ellipsometric properties undergoing post-annealing are still short of studies. In this Letter, the visible near-infrared ellipsometric data of post-annealed Mn_1.95Co_0.77Ni_0.28O₄ thin films were fitted by double Lorenz, Drude, and effective medium approximation models. We found that the Lorenz model can describe the small polaron oscillation, and the Drude model can excellently fit the conductivity produced by small polaron hopping. We found that a large number of carriers hopping between bound states under certain voltage generates a unidirectional flow of quasi-free carriers, which can bring about the same macroscopic properties as free carriers in extended state, and the calculated effective mass of quasi-free carriers is about 150m₀ (m₀ is the effective mass of electrons). We also found that it is mainly Lorentz oscillators and the quasi-free carriers that produce the resonance absorption of electromagnetic waves in the visible near-infrared waveband, and both have electron absorption of lights. These results will perfect the small polaron hopping theory for Mn-Co-Ni-O thin films.

Neural-net computing for interpretation of semiconductor film optical ellipsometry parameters

Optical ellipsometry has been found to be a promising technique for monitoring process parameters, such as film composition and film thickness, of semiconductor wafers grown with molecular beam epitaxy. Whereas it is a straightforward task to calculate ellipsometry angles given the thickness of the film and the refractive indexes of the film and substrate, it is a difficult task to invert that mathematical relationship. However, the process must be inverted if the measured parameters are to be interpreted meaningfully in terms of film composition and film thickness. This paper reports on the use of neural-net computing for the inverse mapping of measured ellipsometry parameters. We used a functional-link net which is very efficient in function approximation. The advantage of using the net, however, is not only its speed, but also because some other net architecture characteristics allow us to perform the task in a holistic manner.