Surface:plasma interactions in GaAs subjected to capacitively coupled RF plasmas

Surface compositional changes in GaAs due to RF plasmas of different gases have been investigated by XPS and etch rates were measured using AFM. Angular Resolved XPS (ARXPS) was also employed for depth analysis of the composition of the surface layers. An important role in this study was determination of oxide thickness using XPS data. The study of surface - plasma interaction was undertaken by correlating results of surface analysis with plasma diagnosis. Different experiments were designed to accurately measure the BEs associated with the Ga 3d, Ga 2P3/2 and LMM peaks using XPS analysis and propose identification in terms of the oxides of GaAs. Along with GaAs wafers, some reference compounds such as metallic Ga and Ga2O3 powder were used. A separate study aiming the identification of the GaAs surface oxides formed on the GaAs surface during and after plasma processing was undertaken. Surface compositional changes after plasma treatment, prior to surface analysis are considered, with particular reference to the oxides formed in the air on the activated surface. Samples exposed to ambient air for different periods of time and also to pure oxygen were analysed. Models of surface processes were proposed for explanation of the stoichiometry changes observed with the inert and reactive plasmas used. In order to help with the understanding of the mechanisms responsible for surface effects during plasma treatment, computer simulation using SRIM code was also undertaken. Based on simulation and experimental results, models of surface phenomena are proposed. Discussion of the experimental and simulated results is made in accordance with current theories and published results of different authors. The experimental errors introduced by impurities and also by data acquisition and processing are also evaluated.

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.