Clustering partially masked images with modified Radon transformation

Abstract

We construct a new procedure for clustering partially masked images from wafer bin maps (WBMs). A WBM is an image of bin codes from circuit probe tests on a wafer after a semiconductor manufacturing process. Certain defect patterns of WBMs provide engineers with information to isolate manufacturing problems. The engineers prefer to see the identified defect patterns in clusters. Then they may match each cluster to a well-known defect type or they could easily see if there are new defect patterns. However, the WBMs may be masked at a large area due to the sequential circuit probe tests for bin codes. WBMs may also have various proportions of noise. There are two major contributions of this research. First, our method of locally iterative interpolation for the image reconstruction of masked WBMs will recover certain parts of the original patterns. Second, we proposed a new modified Radon transformation to extract features for better clustering of partly masked WBMs. We demonstrated our procedure on synthetic WBMs with various defect pattern types and levels of masking through a simulation study. The image patterns of WBMs during simulation were observed from a specific kind of real wafer product.