KERNEL-BASEDDETECTION OF DEFECTS ON SEMICONDUCTORWAFERS

Recent computational methods of wafer defect detection often rely on the difference image between an inspected image and its refer­ ence image, and highly depend on registration accuracy. In this paper, we present a novel method for defect detection in patterned wafers, based on reconstruction of the inspected image from the ref­ erence image using anisotropic kernels. This method avoids regis­ tration between the inspected and reference image and compensates for pattern variations, thus reducing the false detection rate. Ex­ perimental results demonstrate the advantages and robustness of the proposed method. Efficient implementation of the algorithm makes it be suitable for industrial use. We also demonstrate extension of the kernel-based similarity concept to the multichannel Scanning Elec­ tron Microscope (SEM) images.