Boundary extraction in the SEM cross-section of LSI by multiple Gaussian filtering

We proposed a boundary extraction method by utilizing the scale space in order to extract boundaries flexibly from the cross-sectional SEM images. The Gaussian scale space is constructed by convolving the original image with Gaussians of increasing standard deviation, that is, multiple Gaussian filtering. Setting the appropriate range of standard deviations for a target CD measurement part gives a high degree of flexibility to the edge detection method. Though the locations of the edges at the coarse scales may be shifted form their true locations, the true edge location is estimated by tracing the edges detected at coarse scales back to finer scales. By utilizing the edge-location-scale map, the blurring of image acquisition system is also estimated. By applying the proposed method to a model image, the edge location and the blurring estimation accuracies were evaluated. The accuracy in estimated edge locations was less than 0.5 pixels and the blurring very close to the true value was obtained. Next, the proposed method was applied to a cross-sectional SEM image of the DRAM that had a very low contrast boundary to show its validity.