Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks

Abstract

The enhancement of production yield is a continuous challenge in semiconductor manufacturing. Analyzing the spatial defect patterns of previously processed wafers is a key step in identifying the root causes of yield degradation. Predictive modeling approaches have been successful in automated wafer map pattern classification. The classification performance depends significantly on the quantity and diversity of data that can be acquired, which are often limited in practice. In this study, we demonstrate that rotation-based data augmentation can effectively improve wafer map pattern classification when training data are scarce. As rotation is a label-preserving transformation for wafer maps, we construct a convolutional neural network with rotation-augmented training data to render the classification invariant with respect to rotation. This enables us to provide consistent predictions for rotational variations of new wafer maps, thereby achieving higher classification performance. The effectiveness of our method is verified based on real-word data from a semiconductor manufacturer.