A method for detect that bottom topography of phase defects in an EUV photolithography mask multilayer film

Abstract

A method for detect that bottom topography of a phase-type defect of an EUV photolithography mask multilayer film is disclosed. The method includes modeling and detection. In the modeling phase, firstly, the spatial images of blank mask with multilayer phase defects under different illumination conditions are simulated by photolithography simulation software. Fourier transform coherent diffractionimage (FPI) is used to recover that phase of the spatial image, Finally, an artificial neural network is used to establish the detection model, which takes the spatial image information as input andthe mask multilayer phase defect bottom topography parameters as output. In the detection phase, the actual mask space image is collected, and the bottom topography parameters of the actual mask defects are calculated by using the detection model, and the bottom topography of the mask multilayer phase defects is detected. The invention can quickly and accurately detect the topography of the bottomof the phase type defect of the extreme ultraviolet photolithography mask multilayer film.

Description

technical field [0001] The invention relates to an extreme ultraviolet lithography mask, in particular to a method for detecting bottom morphology of a multilayer film phase defect of an extreme ultraviolet lithography mask. Background technique [0002] Photolithography is the core technology of integrated circuit manufacturing. Extreme ultraviolet lithography (EUVL) is regarded as the most promising lithography technology for manufacturing 7nm and smaller node chips. Mask defects in extreme ultraviolet lithography seriously affect the yield rate of chip production, and it is currently impossible to realize the processing and manufacturing of defect-free masks. Therefore, it is of great significance to detect the mask defect and compensate the mask defect according to the detection result. Multilayer defects are unique defects in extreme ultraviolet lithography masks. According to the influence on the reflectivity of the mask, they can be divided into amplitude defects an...

AI Technical Summary

Problems solved by technology

Multilayer defects are unique defects in extreme ultraviolet lithography masks. According to the influence on the reflectivity of the mask, they can be divided into amplitude defects and phase defects. Phase defects are located at the bottom of the multilayer film, resulting in Deformation of the multilayer film, it is difficult for existing instruments to detect the bottom morphology without destroying the structure of the multilayer film

[0003] Prior Art 1 (Prior Art 1: Xu, Dongbo, Peter Evanschitzky, and Andreas Erdmann."Extreme ultraviolet multilayer defect analysis and geometry reconstruction."Journal of Micro / Nanolithography, MEMS, and MOEMS 15.1(2016):014002) using intensity transfer The equation (TIE) restores the phase information of the aerial image, and uses the artificial neural network to construct the relationship between the aerial image information and the phase defect morphology parameters of the multilayer film of the mask, but it focuses on the detection of the defect surface morphology , the detection accuracy of the defect bottom shape is low, and this technology will introduce errors to the movement of the aerial image sensor during the detection process, which will affect the detection accuracy

Prior Art 2 (Prior Art 2: Dou, Jiantai, et al."EUV multilayer defects reconstruction based on the transport of intensity equation and partial least-square regression."International Conference on Optical and Photonics Engineering(icOPEN 2016).Vol.10250. International Society for Optics and Photonics, 2017) uses the intensity transfer equation (TIE) to restore the phase information of the aerial image, and uses the method of least squares regression to construct the phase information of the aerial image and the surface morphology of the phase defect of the mask multilayer film The relationship between the parameters can better detect the surface morphology of defects, but it cannot detect the bottom morphology of defects, and this technology also needs to move the spatial image sensor during the detection process. The movement of the sensor will introduce errors and affect the detection accuracy.

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