Using neural networks to control the process of plasma etching and deposition

Neural architectures are proposed to model and control plasma etching and deposition processes in semiconductor wafer manufacturing. Static and dynamic neural networks are used to develop plant models and inverse models. A single-hidden layer feedforward neural network model learns to identify the system´s input-output relationship. Another single-hidden layer feedforward neural controller learns to model the inverse relationship of the plant. The trained controller, in series with appropriate filters, is then used to control the plasma machine in etching and deposition processes. The paper demonstrates how neural networks can learn both the modeling and control tasks in this nonlinear and complex process