Automating rules-based OPC via scattered data interpolation

In advanced technology nodes, optical proximity correction (OP C) solutions employ a blend of rules-based and model-based treatments to correct the intended circuit design. Rules-based OP C (RB − OP C) is based on a table or series of tables that prescribe discrete treatments for a feature as a function of its surrounding environment. RB−OP C is typically utilized for applications such as etch bias compensation, silicon process bias definition, and OP C model correction. One drawback to defining a rules-based table (RBT) is that the empirical measurements used to populate the table are typically sparse in terms of feature size and pitch, requiring the OP C engineer to fully populate the RBT by other means. Rather than manually interpolating the RBT, scattered data interpolation techniques could be used to automatically populate the RBT at the desired level of granularity. Scattered data interpolation is commonly used in applications such as meteorology and terrain cartography, as it relies on unevenly distributed sparse sample points to characterize the behavior of an entire region. In this study, the proposed technique is applied to correct optical OP C model error on a 28-nm implant level and compared to the baseline process which uses an empirically calibrated OP C model. XOR analysis confirms that the desired RB − OP C treatments are applied after optical OP C model correction. In the next phase of this study, a photomask will be generated containing experimental modules with these treatments, and empirical data will be collected to validate that this technique improves the targeting performance of the optical OP C model alone.