We try to measure lithography line edge roughness (LER) from a noisy SEM image using wavelet analysis. First, we evaluated the edge detection performance of the wavelet multiscale edge detection method without and with denoising by applying them to a modeled secondary electron (SE) signal of photoresist without and with noise. As denoising, the method called soft thresholding was used. Many modulus maxima lines with short lengths for a modeled SE signals with low SNR such as 3 appears and characteristic modulus maxima lines with long lengths do not come out. After denoising, the characteristic modulus maxima lines come out. When SNR was larger than 10, the standard deviation was less than 1 pixel and the average position converged to a point. Then we applied the wavelet multiscale edge detection method to a noisy SEM image of photoresist. LERs (1 sigma evaluated along a distance) along ninety scan lines were measured with the number of average line scans as a parameter. The measured LER for one scan line was determined to be reliable from results of averaging effects and LER for this photoresist pattern was about 3 pixels.
KEYWORDS: Wavelets, Electron beams, Scanning electron microscopy, Beam analyzers, Monte Carlo methods, Inspection, Optical simulations, Wavelet transforms, Defect detection, Imaging systems
We have tried to estimate the electron beam profile from a scanning electron microscope (SEM) image by using Wavelet multiresolution analysis for in-process SEM inspection. At first, an ideal secondary electron (SE) profile for step edge is calculated by using the Monte Carlo simulator. Then the SE profiles observed by the electron beam with Gaussian profiles are simulated with the electron beam diameter as a parameter. Wavelet analyzed results of SE profiles show that it is possible to estimate the size of electron beam profile from the Wavelet coefficient of SE profile. Next we apply the proposed estimation method to SEM images of test pattern. The procedure is as follows. 1) Noise included in SEM images is reduced by using the denosing method by Wavelet transform. 2) The SE profile for edge is extracted from a SEM image and is normalized. 3) The normalized SE profile is decomposed by Wavelet multiresolution analysis. 4) By comparing the obtained Wavelet coefficient of SE profile with the relation between the electron beam diameter and the Wavelet coefficient, the electron beam profile is estimated. Proposed procedure was applied to the SEM image of test pattern to obtain the beam profile.
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