Abstract
Plasma etching of aluminium thin films is characterised using a neural network. For this, etch experiments were designed by means of a statistical experimental design. Relationships between process parameters and etch rate were captured by a back propagation neural network (BPNN). Thepredicted performance of the BPNN model was optimised as a function of training factors. Model predictions were experimentally validated. Parameter effects were examined under a variety of plasma conditions. Radio frequency (rf) power affected the etch rate in different ways, depending onthe Cl2 flow rate. The etch rate variation with rf power (or Cl2 flow rate) was conspicuous only at higher Cl2 flow rates (or rf power). The noticeable effect of BCl3 flow rate at lower N2 flow rate was attributed to an increased concentrationof bombarding ions.
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