Diffusion maps based k-nearest-neighbor rule technique for semiconductor manufacturing process fault detection

Abstract

In the semiconductor industry, traditional multivariate statistical process monitoring methods and pattern classification based detection methods have been developed to detect the semiconductor process faults. However, they do not show superior performance due to the limits of these methods and the unique characteristics of semiconductor processes such as non-linearity and multimodal batch trajectories. This paper presents a novel diffusion maps based k-nearest-neighbor rule (DM-kNN) technique that can reduce data-storage costs and enhance the performance of fault detection by integrating diffusion maps analysis with k-nearest-neighbor rule. DM-kNN takes full advantage of the dimensionality reduction and information preserving properties of DM to extract the low dimensional manifold feature that optimally preserves the intrinsic nonlinear structure of the data set. Then the adapted kNN rule based fault detection method is applied to the low dimensional manifold feature space to detect potential faults. The effectiveness and robustness of DM for dimensionality reduction and feature extraction are verified in simulation experiments compared with other linear and nonlinear dimensionality reduction methods. In addition, DM-kNN is applied to monitor the semiconductor manufacturing process. The fault detection results of the proposed method are demonstrated to be superior to those of the MPCA, FD-kNN, PC-kNN and FS-kNN approaches.