Noise filtering for accurate measurement of line edge roughness and critical dimension from SEM images

Abstract

Measurements of the line edge roughness (LER) and critical dimension (CD) from scanning electron microscope (SEM) images are often required for analyzing circuit patterns transferred onto substrate systems. A common approach is to employ image processing techniques to detect feature boundaries from which the LER and CD are computed. SEM images usually contain a significant level of noise which affects the accuracy of measured LER and CD. This requires reducing the noise level by a certain type of low-pass filter before detecting feature boundaries. However, a low-pass filter also tends to destroy the boundary detail. Therefore, a careful selection of low-pass filter is necessary in order to achieve the high accuracy of LER and CD measurements. In this paper, a practical method to design a Gaussian filter for reducing the noise level in SEM images is proposed. The method utilizes the information extracted from a given SEM image in adaptively determining the sharpness and size of a Gaussian filter. The results from analyzing the effectiveness of the Gaussian filter designed by the proposed method are provided.