Massive e-beam metrology and inspection for analysis of EUV stochastic defect

In the extreme ultraviolet (EUV) lithography process, stochastic defects are randomly generated and can have a significant impact on the yield of high-volume manufacturing (HVM) when printed even at an extremely low probability down to parts per trillion (ppt) level. In this field, electron beam inspection (EBI) tools are regarded as a promising option to detect killer defects with enough capture rate. However, EBI requires a longer inspection time and it is pointed out as the current limitation of EBI. To overcome this limitation, throughput optimization and data collection strategy must be prepared to push the bounds of EBI capability. In this paper, we study the probability of EUV stochastic defect and its statistical signature in massive data using EBI. Tool performance for detecting defects is maximized by investigating the impact of different parameters of scanning electron microscopy (SEM) on throughput and defect capture rate. After performance verification, we demonstrate massive metrology and inspection performance of Die to Database Edge Placement Error (D2DB EPE) to extend the prediction range of stochastic defect probability down to the order of 1 defect/mm2. The method is applied to EBI results on EUV processed pitch 32nm line and space (L/S) pattern to prove the necessity of massive e-beam data analysis of low-level defectivity and intra-field variation.