Kernel-based detection of defects on semiconductor wafers

Recent computational methods of wafer defect detection often rely on the difference image between an inspected image and its reference image, and highly depend on registration accuracy. In this paper, we present a novel method for defect detection in patterned wafers, based on of the inspected image from the reference image using anisotropic kernels. This method avoids registration between the inspected and reference image and compensates for pattern variations, thus reducing the false detection rate. Experimental 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 Electron Microscope (SEM) images.