Content-based segmentation of patterned wafer for automatic threshold determination

This paper introduces a segmentation algorithm suitable for semiconductor wafer images generated by optical inspection tools. The primary application of this work is content-based region segmentation for automatic threshold selection during recipe generation in die-to-die wafer inspection. Structures associated with different functional areas lead to different levels of noise in the difference image during the defect detection process. The ability to automatically create a mask to separate the different structures and materials is necessary to determine local thresholds for each area and thus to improve the signal-to-noise ratio. A supervised segmentation based on the discrete wavelet transform is used to segment a whole die to create a mask. During the inspection, the mask is applied on the difference image, and the threshold is automatically set as a function of the noise within the region and the thresholding coefficient specific to that region. Preliminary segmentation results are very promising. The use of the segmented region in content-based threshold defect detection improves the number of defects detected, and reduces the number of false detections. This paper will show the performance of the segmentation method on optical microscope wafer images, and the subsequent improvement of the defect detection process.