|Modeling of Process Plasma Using a Radial
Byungwhan Kim/Sungjin Park
Transaction on Control Automation, and Systems Engineering, vol. 2, no. 4, pp.268-273, 2000
Abstract : Plasma models are crucial to equipment design and process optimization. A radial basis function network (RBFN) in conjunction with statistical experimental design has been used to model a process plasma. A 24 full factorial experiment was employed to charac-terize a hemispherical inductively coupled plasma (HICP). In characterizing HICP, the factors that were varied in the design include source power, pressure, position of chuck holder, and Cl2 flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature, and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBFN are 0.409 (1012/cm3), 0.277 (eV), and 0.669 (V), for electron density, electron temperature, and plasma potential, respectively. For spatial uniformity data, they are 2.623 (1012/ cm3), 5.074 (eV), and 3.481 (V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network (GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBFN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.
Plasma model, radial basis function network, generalized regression neural network, response surface model