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.

I. Introduction

Defect detection is critical to the manufacturing of semiconductor wafers, yet relying on manual detection is time consuming, expensive and may cause yield ratio loss. A robust automated solution to this problem is essential, as the user will be shown only suspicious regions, thus saving valuable time. Defect detection is challenging as there are no precise characteristics of the possible defects and they may include particles, open lines, shorts between lines or other problems. Defects may belong to the wafer background or to its pattern, and may be predominant or scarcely noticeable. This variety makes it very difficult to perform template matching based on some a-priori features or training database of detects, and therefore encourages the development of unsupervised, data-driven methods.