As the design rules for semiconductor devices continue to shrink, layer-to-layer overlay of actual circuit patterns needs to be measured by using a critical dimension scanning electron microscope (CD-SEM). In addition to the overlay, process variations are making not only local size variations but also local position errors in patterns on the same layer increasingly non-negligible compared to pattern size. In cases where upper and lower patterns partially overlap, these variations and errors cause variations to occur in the positions and visible area of the lower layer patterns in SEM images. As a result, the center position of each lower layer pattern has become difficult to accurately measure resulting in errors in the overlay measurement. In response to this problem, we have developed a method for dynamically placing measurement area cursors for overlay metrology using segmentation technique. This method determines a measurement area cursor by recognizing the position and size of each lower layer pattern through segmentation images generated from the target SEM images using unsupervised deep learning. The performance of this method is evaluated by creating imitated images having the programmed overlay, the local size variations, and the local position errors. We compared the measurement results for the imitated images obtained by the proposed method and a conventional method that uses fixed measurement area cursors and found that the proposed method maintains sensitivity and repeatability even for targets where sensitivity and repeatability degrade with the conventional method due to process variations.
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