New Optimization Strategy for Chemical Mechanical Polishing Process

Abstract

In this study, a systematic approach to achieve a globally optimal Chemical Mechanical Polishing (CMP) process is carried out. In this new approach, the orthogonal array technique adopted from the Taguchi method is used to realize an efficiently experimental design. The RBFNF neural-fuzzy network is then applied to model the complex CMP process. The signal-to-noise ratio (S/N) analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. The globally optimal parameters are successively acquired in terms of the trained RBFNF network. In order to increase CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy performs better than the original approach even though the total processing time is reduced by 1/6.