| dc.description.abstract | In this paper, we analyze the similarities between wafers within the TSMC WM-172K wafer database. We employ a similarity formula to calculate similarity values, which reflect the degree of similarity between two wafers. Next, we establish a similarity threshold, which serves as a reference value for determining whether wafers are similar.
When establishing the similarity threshold, we consider two factors: yield and chip size. We create two new similarity thresholds in addition to the previous one. Using these thresholds, we identify sets of dissimilar wafers within a lot. Furthermore, we define absolute similarity, meaning finding a group of wafers that are identical within the same batch.
Finally, we discuss the practical application of similarity thresholds on actual wafer images. We select the real wafer data set provided by a certain company as the object of the experiment. We use similarity threshold and chamber table to identify batches with similar characteristics and relationships, rapidly determining the presence and location of chamber effects, and early detecting anomalous machines. This leads to improved production efficiency and yield.
Through the analysis of similarity, we can assess the degree of similarity between wafers, aiding in the identification of error patterns and issues that occur within wafers. | en_US |