Characterization of inductively coupled plasma using neural networks

Hemispherical inductively coupled plasma (HICP) in a chlorine (Cl ₂) discharge is qualitatively characterized using neural networks. Plasma attributes collected with Langmuir probe from a HICP etch system include electron density, electron temperature, and plasma potential. Process factors that were varied in a 2⁴ full-factorial experiment include RF power, bias power, pressure, and Cl ₂ flow rate. Their experimental ranges are 700-900 W, 5-10 mtorr, 20-80 W, and 60-120 sccm, for source power, pressure, bias power, and Cl₂ flow rate, respectively. To validate models, eight experiments were additionally conducted. Root mean-squared prediction errors of optimized models are 0.288 (10¹¹/cm³), 0.301 (eV), and 0.520 (V), for electron density, electron temperature, and plasma potential, respectively. Model behaviors were in good agreement with experimental data and reports. For electron temperature and plasma potential, interaction effects between factors were observed to be highly complex, depending on the factors as well as on their levels. A close match was observed between the models of electron temperature and plasma potential