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Article Abstract
Information on the type and morphology of an extreme ultraviolet mask blank defect is of vital importance for mask blank repair and defect compensation. This study introduces an approach for defect type identification and three-dimensional (3D) morphology reconstruction based on photoemission electron microscopy. With the assistance of the neural network transfer learning method, the defect type can be precisely identified, and average error rates of 1.37% and 1.39% were achieved for the morphology parameters of bump and pit defects, respectively. Moreover, both defect-type identification and morphology-parameter reconstruction of phase defects are highly robust. This paper proposes a new, to the best of our knowledge, method for the type identification and 3D morphology characterization of phase defects, providing fundamental data for mask repair.
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