Practical approach to modeling e-beam lithographic process from SEM images for minimization of line edge roughness and critical dimension error

Abstract

Minimization of line edge roughness and critical dimension error in electron-beam lithography J. Vac. Sci. Technol. B 32, 06F505 (2014); 10.1116/1.4899238 Temperature dependent effective process blur and its impact on exposure latitude and lithographic targets using e-beam simulation and proximity effect correction J. Vac. Sci. Technol. B 32, 06F503 (2014); 10.1116/1.4896600 Derivation of line edge roughness based on analytic model of stochastic exposure distributionTwo main factors which limit the minimum feature size and the maximum circuit density achievable by electron-beam (e-beam) lithography are the line edge roughness (LER) and the proximity effect. Since the LER is caused by the stochastic nature of the exposing and development processes, it does not scale with the feature size. Therefore, reducing the LER is essential as the feature size continues to decrease. Accurate modeling of the LER and the proximity effect analytically or via simulation for their minimization is either difficult or costly in many cases. In this study, a practical method for extracting the essential information from SEM images, needed to characterize the e-beam lithographic process, and an effective method for minimizing the LER and critical dimension error based on the extracted information have been developed. The main objective is that the methods utilize only the information extracted from SEM images without having to know the complete setup of e-beam lithographic process. It has been shown that they have a good potential to be further developed into practical and useful tools.

Designing an anisotropic noise filter for measuring critical dimension and line edge roughness from scanning electron microscope images

“…One of the analysis procedures is to employ image processing techniques by which the boundaries of features are detected and compute the CD and LER from the boundaries. [5][6][7][8][9][10][11][12][13][14] Since SEM images tend to be noisy, it is essential to reduce the noise level before the boundary (edge) detection is carried out. The noise filtering has a direct effect on the accuracy of boundary detection, and therefore, it is critical to employ a noise filter optimized for the detection of feature boundaries in SEM images.…”

“…A fixed filter, e.g., median or spatial averaging filter, may be considered 5 but would not be able to consider the abovementioned characteristics properly. In a recent study, 6 a method of designing an isotropic Gaussian filter of which the cutoff frequency and size are adaptively determined based on the power spectra of signal and noise in a given SEM image was proposed and tested with L/S patterns.…”