Designing an Anisotropic Noise Filter for Measuring Critical Dimension and Line Edge Roughness from SEM Images

Abstract

The scanning electron microscope (SEM) is often employed in inspecting patterns transferred through a lithographic process. A typical inspection is to measure the critical dimension (CD) and line edge roughness (LER) of each feature in a transferred pattern. Such inspection may be done by utilizing image processing techniques to detect the boundaries of a feature. Since SEM images tend to include a substantial level of noise, a proper reduction of noise is essential before the subsequent process of edge detection. In a previous study, a method of designing an isotropic Gaussian filter adaptive to the noise level was developed. However, its performance for relatively small features was not so good as for large features, especially in the case of LER. The main objective of this study is to improve the design method such that the accuracy of the measured CD and LER is not deteriorated substantially as the feature size decreases. The new design method allows a Gaussian filter to be anisotropic for the better adaptability to the signal and noise, both of which show a substantial level of directional correlation. The cut-off frequency for the direction normal to features is determined to include most of the signal components and the cut-off frequency in the other direction is set to balance between the signal and noise components to be included. This procedure enables a systematic and easy design of the filter. Also, the method of estimating the noise has been modified for higher accuracy. The performance of the anisotropic Gaussian filter designed by the new method has been thoroughly analyzed using the reference images for which the CD and LER are known. It is observed that the CD and LER errors have been significantly lowered, especially for relatively small features.