Abstract
The processes of semiconductor manufacturing have become more complicated and its yield faces a big challenge. Finding the way of improving the yield is a very critical issue. The direct method to enhance the yield should focus on the manufacturing processes and detecting the unusual situations as soon as possible. Spatial defect recognition can discover the problems of manufacturing processes directly. Defect detection and recognition can trace the problems and find out root cause such that engineers can modify it timely to avoid yield loss.A lot of research works about defects classification have been discussed in the past, but the studied defect patterns are usually single patterns. The researchers ignored the intersecting patterns which are usually much more complicated. Therefore, this research propose to develop an algorithm to deal with the intersecting patterns for helping engineers find out the root cause in the manufacturing rapidly. The objectives of this research are (1) to develop the methodologies to deal with the intersecting patterns including line-shaped cross and curve-shaped cross, (2) to evaluate the performance of different methodologies for intersecting types, and (3) to enhance the performance and save the processing time.The developed methodology has been verified with real data collected from a famous semiconductor company. The experimental results demonstrate that the proposed methodology can not only has high accuracy but also save much time on dealing with the defect identification comparing to human operations.