Parallelized automatic false defect detection using GPUs during mask inspection

Abstract

The mask inspection and review process is a vital part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with a high detection sensitivity and low number of false defects becomes greater. A low number of false defects is desirable as the results of the mask inspection are typically reviewed manually by an operator in the mask shop. However, due to various reasons, the probable mask defects identified by any mask inspection machine could include significant number of false defects. The false defects could be due to registration or focus errors between the defect and reference images (Die-to-Die or D2D comparison), CCD (Charge-coupled device) errors in the camera, noisy pixels etc. These false defects cannot be ignored and require the operator to review them manually before classifying them as false defects. This takes valuable time and effort of the mask inspector and increases the turn-around-time of mask inspection. We propose a software tool which automatically detects most of the false defects generated due to registration and CCD errors in the mask inspection system. It is quite common to find several thousands of defects (real as well as false defects) during mask inspection. We have observed that significant percentage of these false defects are due to registration and CCD errors in defect and reference images during D2D inspection. Automatic detection of registration and CCD errors requires image processing to be done on the defect images. This process is typically, time consuming. However, image processing algorithms are well suited for parallelization. We explore the use of GPUs to speed up the false defect detection process by analyzing the defects in parallel on multiple cores of a GPU. In addition, GPUs are inexpensive, readily available and can be plugged in to any desktop computer which makes it easier to adopt. The proposed GPU based parallel false defect detection feature is integrated into Mask Defect Analysis tool - NxDAT 1 .