Abstract
Fault detection and classification (FDC) plays an important role in monitoring IC manufacturing process. Recently, Lee et al. (2011) proposed a simple and efficient model to analyze a typical profile data. One of main concerns in this model is that the “level shift” parameter may be completely confounding with the process lot-to-lot variation; which usually leads to make a wrong decision on FDC. To overcome this difficulty, this thesis first separates the deviations in profile data into within-lot variations and between-lot variations. Due to the fact that the intrinsic lot-to-lot variation is a natural phenomenon of IC manufacturing process, therefore between-lot-variation shall be removed completely before implementing FDC procedure. In this study, we apply the functional principle component analysis (FPCA) technique to investigate wafer-to-wafer (within-lot) variation. A modified health index (HI) has been constructed. Finally, we also compare the proposed method with that of Lee, et al. (2011). The results demonstrate that our method can efficiently overcome the weakness of Lee, et al. (2011).