Use of neural networks in modeling semiconductor manufacturing processes: an example for plasma etch modeling

System modeling using the plasma etch process is used as a vehicle to demonstrate the application of adaptive nonlinear neural networks in complex process modeling. The system is an active manufacturing process for tantalum silicide plasma gate etching. The model is for a single process recipe, a single technology (1.25 mu m), and a single machine. The model uses a few process signatures to successfully predict the oxide remaining (within 7 AA). The model is pattern density invariant. It is demonstrated that the backpropagation algorithm is adequate for building neural network models of complex nonlinear processes using production databases. The neural network model has a correlation of 0.68 while the statistical model has a correlation of 0.45.<
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