中国物理B ›› 2009, Vol. 18 ›› Issue (6): 2441-2444.doi: 10.1088/1674-1056/18/6/053
王腾1, 李炜2, 陈俊芳2
Li Wei(李炜)a), Chen Jun-Fang(陈俊芳)a)†, and Wang Teng(王腾)b)
摘要: 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.
中图分类号: (Plasma-based ion implantation and deposition)