Enhanced hotspot detection through synthetic pattern generation and design of experiments

摘要

Continuous technology scaling and the introduction of advanced technology nodes in Integrated Circuit (IC) fabrication is constantly exposing new manufacturability issues. Design hotspots are one of such problems, which are a result of complex design and process interactions. These hotspots are known to vary from design to design and foundries expect such hotspots to be predicted early and corrected in the design stage itself, as opposed to a process fix for every hotspot, which would be intractable. Various efforts have been made in the past to address this issue by using a known database of hotspots as a source of information. Most of those works use either Machine Learning (ML) or Pattern Matching (PM) techniques to identify and predict hotspots in new incoming designs. Almost all of those methods suffer from high false-alarm rates, mainly because (i) they are oblivious to the root causes of hotspots, and (ii) a large hotspot database to learn from is generally not available. In this work, we try to address these limitations by using novel hotspot Design of Experiments (DOEs) and synthetic pattern generation approaches. We analyze the effectiveness of the proposed method against the state-of-the-art on a 45nm process, using industry standard tools and designs.