Transverse Deflection for Extreme Ultraviolet Pellicles

Defect control of extreme ultraviolet (EUV) masks using pellicles is challenging for mass production in EUV lithography because EUV pellicles require more critical fabrication than argon fluoride (ArF) pellicles. One of the fabrication requirements is less than 500 mu m transverse deflections with more than 88% transmittance of full-size pellicles (112 mm x 145 mm) at pressure 2 Pa. For the nanometer thickness (thickness/width length (t/L) = 0.0000054) of EUV pellicles, this study reports the limitation of the student's version and shear locking in a commercial tool-based finite element method (FEM) such as ANSYS and SIEMENS. A Python program-based analytical-numerical method with deep learning is described as an alternative. Deep learning extended the ANSYS limitation and overcame shear locking. For EUV pellicle materials, the ascending order of transverse deflection was Ru < MoSi2 = SiC < SiNx < ZrSr2 < p-Si < Sn in both ANSYS and a Python program, regardless of thickness and pressure. According to a neural network, such as the Taguchi method, the sensitivity order of EUV pellicle parameters was Poisson's ratio G Elastic modulus < Pressure < Thickness < Length.

Automated summary

Control of defects in EUV masks using pellicles is challenging due to the stricter fabrication requirements. This study presents the limitations and shear locking issues in commercial finite element methods, and proposes a Python program-based analytical-numerical approach with deep learning as an alternative.