Plasma Etch Prediction Using Genetic Algorithm Based Polynomial Neural Network

Abstract

A GA-PNN was constructed and applied to a model plasma etch process. The etch process was characterised by a statistical experimental design. The performance of GA-PNN was compared with other models including the three types of statistical regression models, conventional backpropagation neural network, polynomial neural network and adaptive network based fuzzy inference system models. In all comparisons, the GA-PNN demonstrated significantly improved predictions. Moreover, the network complexity of conventional PNN could be significantly reduced. This indicates that the GA-PNN is an effective means to construct a predictive model for poorly defined complex systems characterised by the limited data set.

Keywords:

• PLASMA ETCH PREDICTION
• GENETIC ALGORITHM
• GA BASED PNN
• MANUFACTURING INTEGRATED CIRCUITS
• BACKPROPAGATION NEURAL NETWORKS
• FUZZY LOGIC
• POLYNOMIAL NEURAL NETWORK
• ADAPTIVE NETWORKS BASED FUZZY INFERENCE SYSTEM
• OPTIMISATION
• SEM