Paper
5 April 2007 CD-bias evaluation and reduction in CD-SEM linewidth measurements
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Abstract
A new image processing algorithm is proposed and applied to model-based library (MBL) matching to achieve precise and accurate linewidth measurements in critical-dimension scanning electron microscopy (CD-SEM). Image quality is very important in image-based metrology to obtain reliable measurements. However, CD-SEMs are constrained to use poor signal-to-noise ratio images to avoid electron-beam-induced damage. The proposed algorithm is a line edge roughness (LER) compensation averaging algorithm that averages scan lines taking LER into account. The algorithm preserves the edge-bloom shape, which contains 3-dimensional information on the target pattern, while noise is removed by averaging. Applying the algorithm to MBL matching is expected to improve the accuracy of measurement, since MBL matching reduces shape-dependent CD-bias by using the edge-bloom shape. The proposed technique was evaluated by simulation. Precision, accuracy, and relative accuracy were tested and compared to the conventional threshold method. Precision using the proposed technique was 0.49 nm (3σ), which was worse than the 0.23 nm obtained with the conventional method. However, the relative accuracy was 0.5 nm, which was significantly better than the 2.9 nm obtained with the conventional method. As a result, the total measurement error (root mean square of precision and relative accuracy) was reduced from 2.9 nm to 0.7 nm.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maki Tanaka, Chie Shishido, Wataru Nagatomo, and Kenji Watanabe "CD-bias evaluation and reduction in CD-SEM linewidth measurements", Proc. SPIE 6518, Metrology, Inspection, and Process Control for Microlithography XXI, 651848 (5 April 2007); https://doi.org/10.1117/12.710803
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Cited by 10 scholarly publications.
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KEYWORDS
Line edge roughness

Scanning electron microscopy

Monte Carlo methods

Detection and tracking algorithms

Smoothing

Image processing

Signal to noise ratio

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