Fast Dual-Graph-Based Hotspot Filtering

As advanced technologies in wafer manufacturing push patterning processes toward lower subwavelength printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in the generation of many hotspots , which are actual device patterns with relatively large critical-dimension 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 resolution enhancement technique that is applied, unanticipated pattern combinations in rule-based optical proximity correction (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. The golden verification signoff tool using a simulation-based approach has occupied the mainstream and has been able to accurately detect hotspots. However, this approach represents a runtime-quality tradeoff point that is high in quality but also high in runtime. There is also little point in trying to replace the golden signoff tool. We are motivated to develop a low-runtime ldquo prefilter rdquo that reduces the amount of layout area to be analyzed by the golden tool, without compromising the overall quality of hotspot finding. 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 ldquofalse negatives,rdquo i.e., we reliably obtain a superset of actual hotspots, then our method can dramatically reduce the layout area processed by golden hotspot analysis. Our hotspot detection algorithm includes layout graph construction, graph planarization, three-level bridging hotspot detection, and necking hotspot detection. We have tested our flow on several industry test cases. The experimental results show that our method is promising: for benchmark designs in 90-nm and 65-nm technologies, 100% of bridging and open hotspots are detected with few falsely detected hotspots. The average runtime of our method is more than 496 faster compared to the commercial tool.