The market transition from 2D to 3D-NAND in recent years requires strict focus control and monitoring solutions. ASML’s μDBF targets (micro Diffraction Based Focus) enable on-product focus measurement which can be used to optimize scanner correction. Additionally, dense computational focus maps can be generated by combining μDBF measurements with scanner metrology such as non-correctable leveling error. This paper discusses the focus variability observed on memory layers through on product focus monitoring. This work will show how exposure at best focus can be performed for immersion lithography in the case of strong focus fingerprints. Focus monitoring data from μDBF and computational focus metrology will be used to generate and apply corrections on two 3D-NAND layers.
The next generation technology and emerging memory devices require gradually tighter lithographic focus control on imaging critical layers. Especially in case of BEOL process, big PDO (Process Dependent Offset) from large intra-field topography steps affects the process margin directly. There are couple of scanner options to reduce PDO, such as AGILE which provides several benefits. However, for certain use cases the AGILE sensor may not be the optimal solution.
In this paper, we introduce the concept and development background of iFPC (intra-field Finger Print Correction). iFPC is a scanner option that removes the generic 3D fingerprint seen in the leveling data so that both process dependency and actual wafer topography are not followed during wafer exposure.
In addition, we compare the degree of process margin improvement when applying iFPC compared to that of AGILE on a critical layer. The achieved results demonstrate that by applying iFPC it is possible to gain an additional 15~20nm DoF. In other words, on this use case our feasibility suggests that by removing the generic 3D fingerprint seen in the leveling data, it is possible to achieve a better focus performance than when trying to follow the topography during scanning.
In conclusion, we found another good way to improve the process margin through this comparative experiment. Therefore, our next step will be to setup the methodology to select the use cases where iFPC is the optimal solution.
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