An advanced 2d feature transmitted algorithm for mask defect detection

Reticle defect problem caused by different reasons is an unavoidable issue for mask application in fab, which has great influence on the quality and yield of chip product. With the development of chip pitch size, defect management became increasingly important for the higher demand of defect printability. For different patterns of reticle, the impact is quite different owing to the defect location relative to patterns is different which may impact critical dimension (CD) and actual pattern distort on wafer and result in product yield loss. In this study, we used a special algorithm to combine die to die detection results with MEBES data, and defined the defects risk with the energy attenuation (energy loss) of the whole pattern region which different from traditional point-to-point comparison in KLA inspection equipment. Besides, we introduced sensitivity factor(S) for better evaluate the defect risk. The mathematical relationship between the size of the mask defect and the wafer CD are verified by experiments and based on the experimental results, we established the energy loss auto measurement system for monitoring and analysis system of the defect of the hole pattern mask by correlate the size of the defects to the light energy loss rate, which effectively reduces the process risk caused by the mask defect.