极紫外光刻掩模相位型缺陷检测方法  

Method for Inspection of Phase Defects in Extreme Ultraviolet Lithography Mask

作　　者：成维 李思坤[1,2] 王向朝 Cheng Wei;Li Sikun;Wang Xiangzhao(Laboratory of Information Optics and Optoelectronic Technology,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院上海光学精密机械研究所信息光学与光电技术实验室,上海201800 [2]中国科学院大学材料与光电研究中心,北京100049

出　　处：《光学学报》2023年第1期91-101,共11页Acta Optica Sinica

基　　金：国家自然科学基金(U22A2070);国家科技重大专项(2017ZX02101004-002)。

摘　　要：提出了一种基于空间像的极紫外光刻掩模相位型缺陷检测方法,用于检测多层膜相位型缺陷的类型、位置和表面形貌。缺陷的类型、位置和表面形貌均会影响含缺陷掩模的空间像的分布。因此,采用深度学习模型构建含缺陷掩模的空间像与待测缺陷信息之间的映射,利用训练后的模型可从含缺陷掩模的空间像中获取待测缺陷信息。采用卷积神经网络(CNN)模型构建含缺陷空白掩模的空间像和缺陷类型与位置之间的关系,建立用于缺陷类型和位置检测的CNN模型。在获取缺陷的类型与位置后,基于测得的缺陷位置对空间像进行截取,利用截取后的空间像的频谱信息和多层感知机模型获取缺陷表面形貌参数。仿真结果表明,所提方法可对多层膜相位型缺陷的类型、位置和表面形貌参数进行准确检测。Objective Extreme ultraviolet(EUV) lithography has been introduced into high-volume manufacturing(HVM) of chips with a technology node of 7 nm and below. As the technology nodes of chips decrease, the structure of the EUV mask is becoming more and more complex. The defects in EUV masks degrade the mask imaging quality, which is one of the most critical problems affecting the yield of EUV lithography. Phase defects refer to the deformation of the EUV mask multilayer caused by the defects situated at the bottom of the multilayer. Phase defects of nanometer size can lead to a distinct phase shift of the reflected field and seriously degrade the aerial images. Defect compensation methods can be adopted to indirectly compensate for the degradation of imaging quality caused by the phase defects. Accurate inspection of the type, location, and profile of phase defects is the prerequisite for effective defect compensation. A method to inspect the type, position, and surface profile of phase defects in EUV masks on the basis of aerial images is proposed in this paper. The accuracy of the proposed method is verified by simulations.Methods Deep learning models are adopted to construct the mapping between aerial images of defective mask blanks and defect information. After that, the type, location, and profile of phase defects can be obtained from the aerial images of defective mask blanks by the trained models. The inspection model for the type and location of defects is built by the construction of the relationship between the type and location of defects and the aerial images of defective mask blanks with the convolutional neural network(CNN) model. On this basis, the aerial images are intercepted according to the obtained location of defects. The inspection model for the surface profile parameters of defects is constructed with the spectrum information of the intercepted aerial images and the multilayer perceptron(MLP) model.Results and Discussions A test group containing 256 defective mask blanks is utilized to verify th

关 键 词：测量 光刻 极紫外光刻 掩模缺陷检测 空间像 深度学习 

分 类 号：TN305.7[电子电信—物理电子学]