Tool induced shift (TIS) is a measurement error attributed to tool asymmetry issues and is commonly used to measure the accuracy of metrology tools. Overlay (OVL) measurement inaccuracy is commonly caused by lens aberration, lens alignment, illumination alignment and asymmetries on the measured target. TIS impacts total measurement uncertainty (TMU) and tool-to-tool matching, and TIS variation across wafer can account for inaccuracy, if not fully corrected, as it depends on the incoming process condition. In addition, both lot-to-lot and wafer-to-wafer process variation are influenced by TIS in terms of overlay performance, which also includes metrology tool-to-tool efficiency in terms of throughput. In the past, TIS correction was only done using a small sampling, resulting in additional error in the measurement which was not corrected. Hence, a new methodology is explored to improve overlay measurement accuracy by Modeled-TIS (M-TIS). This paper discusses a new approach of harnessing Machine Learning (ML) algorithms to predict TIS correction on imaging-based overlay (IBO) measurements at the after-develop inspection (ADI) step. KLA’s ML algorithm is trained to detect TIS error contributors to overlay measurements by training a model to find the required TIS correction for one wafer. This information, along with additional accuracy metrics, is then used to predict the TIS for other wafers, without having to actually measure the wafers. In this paper, we present the results of a case study focusing on DRAM and 3D NAND production lots.
In the latest 3D NAND devices there is a larger focus on measurement accuracy control, coupled with more traditional minimization of Total Measurement Uncertainty (TMU). Measurement inaccuracy consumes an increasingly significant part of the overlay (OVL) budget, requiring control and optimization.
In this paper we will show the improvement in imaging OVL measurement accuracy using wave tuning (WT) capability combined with advanced algorithms to address 3D NAND process challenges. In addition to new OVL target designs that take advantage of WT capability, we also demonstrate improvement in OVL model residuals through optimization of measurement bandwidth, focus position and number of grab frames. Improvements in precision and tool-to-tool matching are also realized through both optimization of the region of interest (ROI) and splitting measurement areas using a dual-recipe technique.
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