The developed HMM method was trained with the assistance of synthesized rough edges, on a wide spectrum of predefined and controlled noise levels and roughness characteristics. This ensures the method adapts on a variety of LER parameters and noise levels. The edges were then used to validate its effectiveness in a broad range of line patterns. Our results so far specify the characteristics of the training data set which are required to make the method effective in the unbiased LER measurement.
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