The introduction of extreme ultraviolet (EUV) lithography into manufacturing requires changes in all aspects of the infrastructure, including the photomask. EUV reflective masks consist of a sophisticated multilayer (ML) mirror, capping layer, absorber layer, and anti-reflective coating thereby dramatically increasing the complexity of the photomask. In addition to absorber type defects similar to those the industry was forced to contend with for deep ultraviolet lithography, the complexity of the mask leads to new classes of ML defects. Furthermore, these approaches are complicated not only by the mask itself but also by unique aspects associated with the exposure of the photomask by the EUV scanner. This paper focuses on the challenges for handling defects associated with inspection, review, and repair for EUV photomasks. Blank inspection and pattern shifting, two completely new steps within the mask manufacturing process that arise from these considerations, and their relationship to mask review and repair are discussed. The impact of shadowing effects on absorber defect repair height is taken into account. The effect of mask biasing and the chief ray angle rotation due to the scanner slit arc shape will be discussed along with the implications of obtaining die-to-die references for inspection and repair. The success criteria for compensational repair of ML defects will be reviewed.
The systematic error introduced when using 1-D peak detection algorithms to decompose elliptically shaped correlation peaks is investigated. First, an analytical description of this error is derived, and it is shown that this error can lead to a systematic influence of more than one pixel. Additionally, a general linear 2-D Gaussian algorithm based on least squares estimation is presented to allow correlation peak detection of elliptically shaped peaks without any significant bias error. Finally, the performance of the presented algorithms for subpixel displacement estimation, as well as algorithms provided by commercial software packages, is tested with artificial image pairs of random dot patterns.