Intelligent Control of Critical Dimensions in the Semiconductor Industry

The thesis focuses on the design and implementation of a control scheme for a photolithography process. The process requires a tight control to maintain a desired gate critical dimension (CD). The control approach, which is currently in use in most semiconductor facilities is based on operator experience and does not provide satisfactory control on the CD variation. Implementation of an automatic feedback control system in the industry has been difficult because the CD cannot be measured in real-time but only after the process has been completed. In this thesis, a neural network (NN) is used to predict CD based on the input parameters using historical data that are collected at a manufacturing facility. Using neural networks an inverse model of the process is designed and cascaded with the process model to form the feed forward controller. A feedback CD controller that provides a tighter control in the CD variation is obtained by connecting a fuzzy controller in the feedback loop