In semiconductor manufacturing, several measurement data called wafer maps are obtained in the metrology steps, and the variations in the process are detected by analyzing the wafer map data. Hidden processes or equipment affecting the process quality variations can be found by comparing the process tracking history and clustered groups of similar wafer maps; thus, clustering analysis is very important to reduce the process quality variations. Currently, clustering wafer maps are becoming more difficult as the wafer maps are formed into more complex patterns along with high-dimensional data. For more effective clustering of complex and high-dimensional wafer maps, we implement a Gaussian mixture model to a variational autoencoder framework to extract features that are more suitable to the clustering environment, and a Dirichlet process is further applied in the variational autoencoder mixture framework for automated one-step clustering. The proposed method is validated using a real dataset from a global semiconductor manufacturing company, and we demonstrate that it is more effective than other competitive methods in determining the number of clusters and clustering wafer map patterns.