Prediction of the plasma distribution using an artificial neural network
Li Wei(李炜)a), Chen Jun-Fang(陈俊芳)a)†, and Wang Teng(王腾)b)
aSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China; bSchool of Computer, South China Normal University, Guangzhou 510631, China
Abstract In this work, an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR) --- plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers: the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density. The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.
Received: 02 December 2008
Revised: 19 December 2008
Accepted manuscript online:
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