Developing DRC plus rules through 2D pattern extraction and clustering techniques

As technology processes continue to shrink and aggressive resolution enhancement technologies (RET) and optical proximity correction (OPC) are applied, standard design rule constraints (DRC) sometimes fails to fully capture the concept of design manufacturability. DRC Plus augments standard DRC by applying fast 2D pattern matching to design layout to identify problematic 2D patterns missed by DRC. DRC Plus offers several advantages over other DFM techniques: it offers a simple pass/no-pass criterion, it is simple to document as part of the design manual, it does not require compute intensive simulations, and it does not require highly-accurate lithographic models. These advantages allow DRC Plus to be inserted early in the design flow, and enforced in conjunction with standard DRC. The creation of DRC Plus rules, however, remains a challenge. Hotspots derived from lithographic simulation may be used to create DRC Plus rules, but the process of translating a hotspot into a pattern is a difficult and manual effort. In this paper, we present an algorithmic methodology to identify hot patterns using lithographic simulation rather than hotspots. First, a complete set of pattern classes, which covers the entire design space of a sample layout, is computed. These pattern classes, by construction, can be directly used as DRC Plus rules. Next, the manufacturability of each pattern class is evaluated as a whole. This results in a quantifiable metric for both design impact and manufacturability, which can be used to select individual pattern classes as DRC Plus rules. Simulation experiment shows that hundreds of rules can be created using this methodology, which is well beyond what is possible by hand. Selective visual inspection shows that algorithmically generated rules are quite reasonable. In addition to producing DRC Plus rules, this methodology also provides a concrete understanding of design style, design variability, and how they affect manufacturability.