Development of taxonomy for classifying defect patterns on wafer bin map using Bin2Vec and clustering methods

Abstract

A wafer consists of several chips, and serial electrical tests are conducted for each chip to investigate whether the chip is defective. A bin indicates the test results for each chip with information on which tests the chip failed. A wafer bin map (WBM) shows the locations and bins of the defects on the wafer. WBMs showing spatial patterns of defects usually result from assignable causes in the wafer fabrication process; hence, they should be classified in advance. The existing defect-pattern taxonomies do not consider bins, although useful information can be obtained from them. We propose a taxonomy that consists of the shape, size, location, and bin dimensions. The bin dimension is developed using Bin2Vec method, which determines RGB (red-green-blue) code for each bin according to the spatial similarity between bins. Three levels of the bin dimension are defined by analyzing a large number of WBMs using Bin2Vec and clustering methods. Compared with the existing taxonomies, the proposed taxonomy has the advantage of identifying major bins of defect patterns, new defect patterns, and non-critical defect patterns. A high-quality training dataset was obtained using the proposed taxonomy; consequently, a defect pattern classification model with satisfactory classification performance could be obtained.