Kim Sang-Kon
The Faculty of Liberal Arts, Hongik University, Seoul 04066, Republic of Korea.
Materials (Basel). 2023 Apr 29;16(9):3471. doi: 10.3390/ma16093471.
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 μm transverse deflections with more than 88% transmittance of full-size pellicles (112 mm × 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<Elastic modulus<Pressure<Thickness<Length.
在极紫外（EUV）光刻中，使用防护膜来控制EUV掩膜的缺陷对于大规模生产具有挑战性，因为EUV防护膜的制造要求比氟化氩（ArF）防护膜更为苛刻。制造要求之一是在2 Pa压力下，全尺寸防护膜（112 mm×145 mm）的横向挠度小于500μm，透过率大于88%。对于纳米厚度（厚度/宽度长度（t/L）=0.0000054）的EUV防护膜，本研究报告了基于商业工具的有限元方法（FEM）（如ANSYS和西门子）中存在的学生版本局限性和剪切锁定问题。介绍了一种基于Python程序的深度学习解析数值方法作为替代方案。深度学习扩展了ANSYS的局限性并克服了剪切锁定。对于EUV防护膜材料，在ANSYS和Python程序中，无论厚度和压力如何，横向挠度的升序排列均为Ru<MoSi2=SiC<SiNx<ZrSr2<p-Si<Sn。根据神经网络（如田口方法），EUV防护膜参数的灵敏度顺序为泊松比<弹性模量<压力<厚度<长度。
