Active cluster annotation for wafer map pattern classification in semiconductor manufacturing

In semiconductor manufacturing, wafer map pattern classification (WMPC) is important for ensuring good manufacturing quality because the defect type on a wafer map provides important information for determining defect causes. To construct a high-performance WMPC model, a large amount of labeled training data is required, which entails a high annotation cost for engineers. To reduce the annotation cost, an active learning framework has been investigated, in which annotation is conducted at the individual wafer map level. If wafer maps can be grouped into clusters based on the similarity of defect patterns, then annotation can be performed at the cluster level rather than at the wafer map level, thereby affording cost effectiveness. Based on the cluster-level annotation, we propose an active cluster annotation to obtain a high-performance WMPC model with reduced annotation cost. For a dataset annotated only for a small subset of wafer maps, clustering is first conducted for unlabeled wafer maps. In an active learning iteration, a convolutional neural network (CNN) is constructed with labeled wafer maps. Subsequently, the cluster-level classification uncertainties of the CNN for unlabeled wafer maps are calculated. With the uncertainties, the query clusters are selected and annotated by an engineer at the cluster level. The performance of the CNN is improved cost-effectively by repeating these iterations. We demonstrate the effectiveness of the proposed method through experiments on real-world data from a semiconductor manufacturer. In addition, we show that cluster-level annotation is a robust annotation method that can yield consistent labels.