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.
Mask manufacturers are continuously challenged as a result of the explosive growth in mask pattern data volume.
This paper presents a new pipelined approach to mask data preparation for inspection that significantly reduces the
data preparation times compared to the conventional flows used today. The focus of this approach minimizes I/O
bottlenecks and allows for higher throughput on computer clusters. This solution is optimized for the industry
standard OASIS.MASK format.
These enhancements in the data processing flow, along with optimizations in the data preparation system
architecture, offer a more efficient and highly scalable solution for mask inspection data preparation.
OASIS (P39) specification imposes few restrictions on file structure and does not enforce the use of standard properties
and features such as strict mode. As a natural consequence, P39 readers must be general enough to cover all possible
types of input, and this leads to inefficiencies. In contrast, OASIS.MASK (P44) specification, which is a formal subset of
P39, has restrictions on file structure and enforces the usage of properties, both standard and user mandatory ones. In
addition to this, strict mode is mandatory and new features like Localization are added.
Even though it is possible to read a P44 file using general P39 readers, such readers do not take advantage of the special
P44 characteristics and therefore remain inefficient, especially for applications needing fast random access to arbitrary
extents of data within the file. In this paper we explain the concepts that should be considered in the design of an
efficient P44 reader and present Synopsys CATS<sup>R</sup> implementation. Experimental results comparing CATS<sup>R</sup> P44
specific reader against standard P39 readers show significant performance benefits.
Polygon sizing is required during Mask Data Preparation in order to generate derived layers
and as process bias to account for edge effects of etching. Two main features are required for
polygon sizing algorithms to be useful in Mask Data Preparation software: correctness to avoid data
corruption and clipping of the projection of acute angle vertices to limit connectivity modifications.
However, current available solutions are either based on heuristics, producing corrupted results for
certain input, or based on algorithms which may fail to maintain original design's connectivity for
certain input. A novel algorithm including customizable clipping is presented.