Aim: Unbiasing of roughness measurements is best accomplished by taking advantage of the frequency characteristics of the noise to measure and subtract it out. This requires the ability to detect edges in a noisy SEM image without the use of standard image filtering techniques
. Approach: A physics-based inverse linescan model is used to robustly detect edges in high-noise SEM images without the use of filtering or image averaging. To validate the efficacy of SEM noise measurement and subtraction, rough features were measured under a wide variety of SEM settings, including number of frames of averaging and voltage.
Results: In all cases, the vast majority of the measurement bias was properly subtracted out. Over a wide range of SEM settings the biased roughness varied by more than a factor of two, but the unbiased linewidth roughness varied by only a few percent.
Conclusions: The approach of inverse-linescan edge detection followed by noise measurement and subtraction leads to reliable estimates of the true (unbiased) line-edge and linewidth roughness of features on the wafer. These unbiased estimates are quite insensitive to metrology tool settings over a reasonable range of values.