Proceedings Article | 28 June 2013
KEYWORDS: Detection and tracking algorithms, Tolerancing, Data conversion, Distance measurement, Algorithm development, Computed tomography, Design for manufacturing, Resolution enhancement technologies, Optical proximity correction, Manufacturing
It is common knowledge that DFM guidelines require revisions to design data. These guidelines impose the need for
corrections inserted into areas within the design data flow. At times, this requires rather drastic modifications to the data,
both during the layer derivation or DRC phase, and especially within the RET phase. For example, OPC. During such
data transformations, several polygon geometry changes are introduced, which can substantially increase shot count,
geometry complexity, and eventually conversion to mask writer machine formats. In this resulting complex data, it may
happen that notches are found that do not significantly contribute to the final manufacturing results, but do in fact
contribute to the complexity of the surrounding geometry, and are therefore undesirable.
Additionally, there are cases in which the overall figure count can be reduced with minimum impact in the quality of the
corrected data, if notches are detected and corrected. Case in point, there are other cases where data quality could be
improved if specific valley notches are filled in, or peak notches are cut out. Such cases generally satisfy specific
geometrical restrictions in order to be valid candidates for notch correction.
Traditional notch detection has been done for rectilinear data (Manhattan-style) and only in axis-parallel directions. The
traditional approaches employ dimensional measurement algorithms that measure edge distances along the outside of
polygons. These approaches are in general adaptations, and therefore ill-fitted for generalized detection of notches with
strange shapes and in strange rotations.
This paper covers a novel algorithm developed for the CATS MRCC tool that finds both valley and/or peak notches that
are candidates for removal. The algorithm is generalized and invariant to data rotation, so that it can find notches in data
rotated in any angle. It includes parameters to control the dimensions of detected notches, as well as algorithm tolerances
and data reach.
