A golden-block-based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. The technique has a learning ability that can create a golden-block database from the wafer image itself, then modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrive, we do not have to recalculate their periods and building blocks. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. Also, the storage overhead of golden templates is reduced significantly by storing golden blocks only.