


	Mr. Fei Wang Application Development Manager WWTS Hermes Microvision Inc.

Profile
-Education MSEE MBA

-Experience Application Development Manager in Hermes Microvision
Abstract:

E-beam Defect inspection of EUV Masks and Wafers

Defectivity of EUV reticles and their impact on wafer printing has continued to plague the future adoption of EUV lithography. This study presents a methodology for assessing the impact of defect detection of EUV reticles by e-beam inspection of both the reticle and the exposed photoresist on a wafer. The programmed defect test reticle is designed with both additive and subtractive features of varying size and shape, which are superimposed on a variety of features ( ex: line/space, holes, and brick patterns). To test defectivity of the EUV reticle, e-beam inspection images from an HMI eXplore® 5400 are compared to the post OPC mask shapes in a die-to-database mode. We show that the e-beam system can detect defects with sensitivity and capture rate comparable to a standard die-to-die measurement. An example of the capability of the e-beam system for detection of sub-20nm defect size feature on a variety of programmed defect design shapes and defect types using an automated defect classification is shown in Figure 1. The auto-detect defect capability on both the mask absorber and non-absorber is demonstrated in Figure 2. The Albany Nanotech Alpha Demo EUV Tool was employed to expose wafers with the inspected test reticle (on HMI eXplore® 5400 mask inspection tool) having the same programmed defect structures. The printability of defects on the EUV reticle can be assessed by comparing e-beam inspection images from an HMI eScan® 320Xp (wafer inspection tool) to the print simulation in die-to-database mode using the HMI SuperNova™ system. We show that the e-beam defect inspection on EUV resist wafer for both die to die and die to database (print simulation) are at comparable sensitivities. Figure 3 shows an example of the capability of sub-10nm defect size detection on wafer for a variety of design shapes and defect types using automated defect classification.