A simulation study of cleaning induced EUV reflectivity loss mechanisms on mask blanks

Abstract

It is widely recognized in the semiconductor industry that getting to defect-free extreme ultraviolet (EUV) mask blanks is critical in achieving high volume chip manufacturing yield beyond the 22 nm halfpitch node. Total defectivity of an EUV mask blank depends on the defectivity of substrate and finished mask blank. Finished mask blanks are normally subjected to a cleaning process to get rid of the loosely adhered particles on the top. This is normally done in a spin-spray mask cleaning tool using traditional mask cleaning processes. It is important that this cleaning process does not degrade the properties of the multilayer blank or introduce additional particles or pits during the process. However, standard cleaning processes used to clean multilayer blanks result in EUV reflectivity loss, loss of uniformity in reflectivity, increased roughness and adds pits and particles on mask blanks. The standard cleaning process used consists of multiple steps, each of which may cause the oxidation of Ru capping layer as well as other underlying bilayers, etching of the multilayer stack and increased roughness of the bilayers thus leading to a loss in EUV reflectivity. It is a challenging task to experimentally correlate the processing steps to the resulting damage and to quantify the reflectivity loss. Furthermore, due to the high cost of materials we have not been able to do extensive experiments to determine the root cause of problems. In this work, we have combined mask blank cleaning using standard processes, TEM cross section studies and simulations to quantify the impact of the multilayer oxidation, etching and roughness on the EUV reflectivity loss and mask blank degradation.

Evaluating printability of buried native extreme ultraviolet mask phase defects through a modeling and simulation approach

“…4 and 5 are at best focus conditions, i.e., defocus equal to zero. Here, we would like to note that we performed simulation studies 29 to look at the impact of the defect (Gaussian-shaped) volume on aerial image contrast and observed a wide range of contrast values for the same defect volume, thus further demonstrating that both the width and height information of the defect is needed to fully characterize it in terms of predicting its printability behavior, and volume information alone is not sufficient and can even be misleading.…”