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Statistical optimization of Canny edge detector for measurement of fine line patterns in SEM image

Yoshihiro Midoh, Katsuyoshi Miura, Koji Nakamae and Hiromu Fujioka

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For the purpose of developing a recognition system for fine line patterns in the LSI, a statistical optimization method of the Canny edge detector for scanning electron microscope (SEM) images is proposed. The analysis of variance (ANOVA) is applied to the estimation of parameters in the SEM image: the grey level in the background, contrast, the line width and the noise level. These parameters are used to optimize statistically the Canny parameters, a scale parameter of the smoothing filter and a binarization threshold, by using noise characteristics of the SEM image. The proposed method is applied to model images and a real SEM image to show its validity.


PACS

42.30.Sy Pattern recognition

07.05.Pj Image processing

02.60.Pn Numerical optimization

85.30.De Semiconductor-device characterization, design, and modeling

85.40.Qx Microcircuit quality, noise, performance, and failure analysis

MSC

62J10 Analysis of variance and covariance

Subjects

Computational physics

Electronics and devices

Semiconductors

Instrumentation and measurement

Optics, quantum optics and lasers

Dates

Issue 2 (February 2005)

Received 29 June 2004, in final form 19 October 2004

Published 21 January 2005



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