## 2 Literature Review  
The mainstream research on WBM analysis has focused on the discovery of meaningful patterns or clusters from bad wafers. In this framework, bad wafers are manually filtered first, and then various analytical techniques are applied to find significant patterns or clusters based on their WBMs [16; 17; 18; 19; 20]. In the early 2000s, [PERSON] et al. [16] found wafer clusters by applying a 3 \(\times\) 3 median filter to the WBM to replace isolated chips with the median value of neighboring chips. Since median filtering has a blurring effect, it becomes difficult to discover defective wafer patterns with high precision. Instead of a median filter, [PERSON] [17] applied a mean filter to the WBM for the purpose of denoising, and found defective wafer clusters. As a clustering method, they employed spectral clustering based on fuzzy C-means (FCM) with kernel principal component analysis, given that FCM-based spectral clustering is known to be robust against outliers. Recently, a neural-network based method that replaces the previous kernel principal analysis has also emerged to extract the principal singular triplet (PST) of a cross-correlation data, such as wafer images [21]. In the 2010s, [PERSON] et al. [18] proposed the segmentation with detection and small cluster removal (SDC) algorithm. The SDC algorithm automatically generated features for clustering WBMs by local yield conversion (LYC) method, which extracted the mean value of the \"passed\" and \"failed\" neighboring chips around the targeted chip. Through the cut-off filtering process, the LYC can extract the dominant wafer defect patterns. [PERSON] et al. [19] proposed the Dominant Defective Patterns Finder (DDPFinder) algorithm that maintains the spatial dependence of defect patterns throughout the WBM, and considers the relative coverage of defect patterns during the clustering process. They randomly selected \(k\) sample dies for cluster centroid initialization, which resulted in \(k\)-Voronoi regions in k-means clustering (KMC). The dominant defective patterns were extracted by the centroids of the Voronoi region to which defective wafers were assigned. They compared the performance of DDPFinder and eight benchmarked models (i.e., simplified subspace regression network, randomized general regression neural network, support vector machines, and four artificial neural networks (ANNs, i.e., general regression neural, radial basis function, probabilistic neural network, and multi-layer perceptron). The experimental results showed that DDPFinder outperformed the benchmarked methods with an accuracy of 99.78% based on a predefined defect wafer dataset. [PERSON] and [PERSON] [20] proposed a framework that removed random noise to find robust defective WBM pattern clusters based on a cellular neural network. To do so, they first used a moment-invariant method, which is one of the shape recognition techniques widely used in the field of image processing, to extract rotation and size-invariant features. Based on the extracted features, they applied a neural network-based adaptive resonance theory (ART) to find WBM clusters. Their method showed 97% clustering purity even with high defective rate in the highly noisy environment. The experimental results also showed that the proposed method resulted in higher purity, diversity, specificity, and efficiency than the four existing clustering methods (Ward, KMC, self-organizing map (SOM), and spectral). However, the main disadvantage of this method is that it cannot find combined defective patterns. In addition to the task of finding significant defective WBM patterns, some studies have attempted to automate the process of finding defective patterns and assigning the WBMs of newly processed wafers to one of these patterns, which is formulated as a multiclass classification problem. [PERSON] et al. [5] proposed a hybrid clustering method to define the characteristics of WBM clusters and assign individual WBMs to one of these discovered patterns.