Detecting focus-sensitive configurations during OPC

Model-based optical proximity correction (OPC) calculates pattern adjustments by simulating the layout with calibrated lithography and process models. OPC can only correct systematic lithography deviations, those error components that repeat chip to chip. OPC cannot compensate random deviation error components from unpredictable process variations, such as defocus and dose. However the ranges of variation from random effects is predictable, and OPC can optimize correction shapes to minimize this range where possible. Current techniques for supporting this optimization involve applying a set of models covering the range of expected process variations in defocus and exposure. Variation is assessed by comparing process corner-point model evaluations. Because there is a significant runtime cost simulating multiple process conditions, most production OPC jobs use a single, representative model (typically the "nominal" process condition) aided with rules and other heuristics to help handle process window effects. In this paper the application of a new type of model that can be used to predict process variation with a single simulation call. The model involved in these studies targets pattern behavior as the focus offset deviates from the nominal focus setting. Used in conjunction with a nominal process model, this model can support process-window optimized OPC without the need for multiple models at various defocus settings. This model can also be used by itself to assess the defocus robustness of any configuration before or after OPC, thereby supporting efficient model-based layout verification.