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Photomask inspection requires a combination of high resolution and high throughput. Scanning electron microscopy (SEM) has excellent resolution but at high throughput yields noisy images. Hence we are developing algorithms for extracting pattern information from noisy SEM images.
One big challenge in processing SEM images is edge extraction. SEM images have their own characteristics so many existing edge extraction algorithms based on gradient signal analysis do not work well in that they either yield strong signal for non-edge areas or yield weak signal for true edge areas. We describe several new edge extraction algorithms targeting noisy SEM images. The essence of these new algorithms is analyzing the "ridge" signal, i.e., the bright stripes.
We first propose edge extraction based on second-order polynomial regression. Based on the observation that the pixel values around edges in SEM images behave approximately as second-order polynomial functions of coordinates, we compute the "ridge" signal using the coefficients of such polynomial functions obtained from regression. This algorithm generally yields very accurate estimation of the edge locations, especially for straight edges.
In the approach based on second-order polynomial regression, it is implicitly assumed that the edge is (approximately) straight. We thus propose a further improvement on this algorithm and assume that edge shapes can be well approximated by second-order curves, even at sharp turns. This approximation leads to a fourth-order polynomial regression with better performance around edges with a sharp turn.
A third algorithm is based on image segmentation. Image segmentation, which is mostly used in image content analysis, is defined as the partition of a digital image into multiple regions (sets of pixels) so that the objects of interest are separated from the background. In our approach, we adapt image segmentation to edge extraction. In particular, we apply a fast segmentation algorithm to separate the bright area from the dark area, and use the difference of average pixel values as the "ridge" signal. The advantage of this approach is that no assumption on the edge shape is involved and the computational complexity is low.
Finally, we propose a hybrid algorithm combining the segmentation approach and the polynomial regression approach, yielding a "segmentation-assisted" algorithm that incorporates the advantages of both approaches. Simulation on a wide range of SEM image types yields quite satisfactory results, even for very noisy images. We will present detailed algorithm flows and demonstrate extraction results from real images.
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