Intelligent control of critical dimension in photolithography process

This paper focuses on the design of a control scheme for a photolithography process. The process requires a tight control to maintain a desired gate critical dimension (CD). A neural network is used to predict the CD based on measurements of the thickness, reflectivity, refractive index, and dose. The neural network is trained using historical data that are collected at a manufacturing facility. In addition, a neural network-based inverse model of the process is developed. The inverse model is cascaded with the process model to form a feedforward controller. A feedback CD controller that provides a tighter control in the CD variation is obtained by including a fuzzy controller in the feedback loop