Multi-channel wafer defect detection using diffusion maps

Detection of defects on patterned semiconductor wafers is a critical step in wafer production. Many inspection methods and apparatus have been developed for this purpose. We recently presented an anomaly detection approach based on geometric manifold learning techniques. This approach is data-driven, with the separation of the anomaly from the background arising from the intrinsic geometry of the image, revealed through the use of diffusion maps. In this paper, we extend our algorithm to 3D data in multichannel wafer defect detection. We test our algorithm on a set of semiconductor wafers and demonstrate that our multiscale multi-channel algorithm has superior performance when compared to single-scale and single-channel approaches.