@inproceedings{166843f80daf4d028230b2a20f89ae29,
title = "Multi-channel wafer defect detection using diffusion maps",
abstract = "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.",
keywords = "Anomaly detection, Diffusion maps, Dimensionality reduction, Multiscale representation, Wafer defect detection",
author = "Gal Mishne and Israel Cohen",
note = "Publisher Copyright: {\textcopyright} Copyright 2015 IEEE All rights reserved.; 2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 ; Conference date: 03-12-2014 Through 05-12-2014",
year = "2014",
doi = "10.1109/EEEI.2014.7005897",
language = "الإنجليزيّة",
series = "2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014",
booktitle = "2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014",
}