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We developed a new measurement method enabling to quantitatively and accurately evaluate 2D pattern shapes, which becomes critical in patterning control of Metal layer patterns transferred by Litho-Etch-Litho-Etch (LELE) process. In LELE, a split patterning of a Metal-A (MA) layer and a Metal-B (MB) layer makes patterning control more challenging. Hence, it is essential to evaluate the shape of transferred patterns after final etching in order to verify that the patterning control of MA and MB layer patterns is performed within an allowable budget. For this, our Pattern Shape Quantification (PSQ) method [1][2][3], which enables to measure dimensional difference of the transferred pattern shape from their target-design, is an effective metrology. Patterns transferred through a LELE process contain the effects of two types of shape modifications. The first is the fidelity of the individual pattern shapes (e.g. pattern-end pull-back or push-out) whose determinative factors are adopted design (e.g. OPC and SRAF), process condition (of e.g. lithography and etching), etc. The second is the shift in position between MA and MB patterns induced by Pattern Placement Error (PPE) of MB with respect to MA. That means that the edge-placement errors (EPE) in the final pattern are not only due to the fidelity of the transferred pattern shape, but are also impacted by the PPE. Also, a space between MA and MB patterns will be affected by the PPE as well. A failure to maintain a required minimum space between patterns could lead to a leak-current between patterns (and hence directly affect device performance), so it is important that the PPE can be measured accurately. Therefore, we developed a method to measure local PPE in actual device patterns, from CD-SEM images, that also outputs a pattern-contour in which this PPE has been removed. Utilizing such a pattern-contour into the PSQ method enables to quantitatively determine the fidelity of transferred pattern shape solely induced by the 1st shape modification, while providing PPE data from the device patterns themselves. We believe that a high-quality patterning control (by e.g. optimization of process condition) of MA and MB can be performed only by using such a measurement result. This paper demonstrates and discusses the capability and effectiveness of our newly developed method.
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