Modeling of a hemispherical inductively coupled plasma using neural network

In this study, a hemispherical inductively coupled plasma (HICP) was modeled by using a neural network called a radial basis function network (RBFN). The prediction performance of RBFN models were optimized by using a genetic algorithm. Using a Langmuir probe, experimental data were collected from the HICP equipment of 10 turns. For a systematic modeling, plasma discharge was characterized by using a statistical experiment. The process parameters involved include a radio frequency source power, pressure, position of probe tip, and Cl2 flow rate. The plasma characteristics modeled include plasma density and electron temperature. From the optimized models, 3D plots were generated to explore parameter effects. Plasma density (or electron temperature) was the most strongly dependent on the tip position. The effect of source power on plasma density was almost independent of Cl2 flow rate. The effect of pressure was inclined to slightly decrease plasma density. Unlike in other plasma sources, electron temperature was little affected by pressure. The effect of Cl2 flow rate of increasing electron temperature was the most significant under higher plasma density.