Abstract
The semiconductor industry historically has been an extremely dynamic and high-growth business. Yield improvement is the important element to ensure the profitability. Defects incumber yield and increase the cost of manufacturing, so the defect inspection and recognition are vital in wafer fabrication. The development of recognition methodology is also the essential ingredient of yield enhancement.This research intends to propose a high-efficiency solution of the pattern-related defects recognition based on the hybrid approach of pattern recognition. In this research we classify pattern-related defects into different categories including line, curve, ring, local, radial, repeat, and die edge. In addition to single-pattern problems we discussed before, this research also intends to solve the problems of multi-pattern recognition such that the practical applications can further be improved. There are five types of relationship in multi-pattern situation defined in this research, including separation, branch, crossover, encirclement and overlap. After transforming coordinate data into image format and using image processing techniques to eliminate the unsystematic noise and set grids to get structural information for further analysis. The procedures of hybrid identification technique performed in this methodology including templates matching, primitive separation, feature extraction and feature selection. By setting feature vectors into feature space and applying FCM theory to clustering, it is expected to reach the goal of description and classification.The proposed methodology is verified with real industrial data and the experimental results show the advantages of high accuracy and short executing time.