Proceedings Article | 20 October 2006
KEYWORDS: Critical dimension metrology, Lithography, Optical proximity correction, Edge detection, Optical lithography, Detection and tracking algorithms, Process modeling, Metals, Model-based design, Resolution enhancement technologies
As advanced technologies in wafer manufacturing push patterning processes toward lower-k1 subwavelength
printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in
generation of many hotspots, which are actual device patterns with relatively large CD and image errors with
respect to on-wafer targets. Hotspots can be formed under a variety of conditions such as the original design being
unfriendly to the RET that is applied, unanticipated pattern combinations in rule-based OPC, or inaccuracies
in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of
a device, device performance and parametric yield can be significantly degraded. Previous rule-based hotspot
detection methods suffer from long runtimes for complicated patterns. Also, the model generation process that
captures process variation within simulation-based approaches brings significant overheads in terms of validation,
measurement and parameter calibration.
In this paper, we first describe a novel detection algorithm for hotspots induced by lithographic uncertainty.
Our goal is to rapidly detect all lithographic hotspots without significant accuracy degradation. In other words,
we propose a filtering method: as long as there are no "false negatives", i.e., we successfully have a superset of
actual hotspots, then our method can dramatically reduce the layout area for golden hotspot analysis. The first
step of our hotspot detection algorithm is to build a layout graph which reflects pattern-related CD variation.
Given a layout L, the layout graph G = (V, Ec union Ep) consists of nodes V, corner edges Ec and proximity edges
Ep. A face in the layout graph includes several close features and the edges between them. Edge weight can be
calculated from a traditional 2-D model or a lookup table. We then apply a three-level hotspot detection: (1)
edge-level detection finds the hotspot caused by two close features or "L-shaped" features; (2) face-level detection
finds the pattern-related hotspots which span several close features; and (3) merged-face-level detection finds
hotspots with more complex patterns. To find the merged faces which capture the pattern-related hotspots,
we propose to convert the layout into a planar graph G. We then construct its dual graph GD and sort the
dual nodes according to their weights. We merge the sorted dual nodes (i.e., the faces in G) that share a given
feature, in sequence. We have tested our flow on several industry testcases. The experimental results show that
our method is promising: for a 90nm metal layer with 17 hotspots detected by commercial optical rule check
(ORC) tools, our method can detect all of them while the overall runtime improvement is more than 287X.
