A new approach for semiconductor parameter extraction using cathodoluminescence and artificial neural networks.

In this paper, a new parameter extraction technique that jointly extracts four semiconductor-related parameters from theoretical/experimental cathodoluminescence data collected as a function of electron-beam energy is presented. The extraction technique is based on feed-forward artificial neural networks (ANN) where the ANN is trained to learn the inherent relationship between the input parameters (absorption coefficient α, diffusion length L, dead layer thickness Zt, and relative quantum efficiency Q) and the output parameter (CL intensity versus electron beam energy). After the training of the ANN, it is possible to observe the reverse process and extract the four parameters from any CL curve using an exhaustive search method. One of the main advantages of the proposed method is that the optimum set of values for the four parameters (α, L, Zt, Q) are obtained because the exhaustive search is performed in the search space spanned by all four parameters. Computational results on an n-type GaAs free defect semiconductor sample show that a unique set of parameter values with errors less than 5.5% from the nominal values can be obtained for each set of the experimental data points using the proposed algorithm.