Early Defect Detection Using Clustering Algorithms

Product quality is a crucial issue for manufacturing companies, so it is essential to take note of any emerging product defects. In contrast to the use of traditional methods, the "modern" constantly evolving data mining methods are now being more frequently used. The main objective of this paper is to detect the potential cause or the area of the production process where the majority of product defects arise. The dataset from the semiconductor manufacturing process has been used for this purpose. First, it was necessary to address dataset quality. Significant multicollinearity was found in the data and to detect and delete the collinear variables, correlations and variance inflation factors have been used. The MICE-CART method has been used for the imputation because the original dataset contained more than 5% of random missing values. In further analysis, the K-means clustering method has been used to separate the failed products from the flawless ones. Following this, the hierarchical clustering method has been used for the failed product to create groups of product defects with similar properties. For the optimal number of clusters, the determination of the BIC method has been used. Five clusters of products have been made although only three can be classed as important for further analysis. These groups of products should be directly subjected to the analysis in the production process, which can assist in identifying the source of scarcity.