The effect of the OPC parameters on the performance of the OPC model

Abstract

Model based optical proximity correction (MB-OPC) is essential for the production of advanced integrated circuits (ICs). As the speed and functionality requirements of IC production necessitate continual reduction of the critical dimension (CD), there is a heightened demand for more accurate and sophisticated OPC models. The OPC is applied to the design data through a rule deck. The parameters in this rule deck, which we will call "setup parameters", describe the fundamental way in which the OPC engine will distinguish which edges to move, their restrictions to movement, and how the targets for the OPC are chosen. The optimization of these setup parameters, by customizing how the OPC engine should treat specific designs, is an essential step that is performed in order to maximize the benefit of the OPC model. Improper or deficient selection of the setup parameters strongly affects the success or failure of the OPC model and engine to achieve the desired design shapes. In this paper, the ability of setup parameter optimization to compensate for a weak OPC model, or conversely, how inadequately selected setup parameters can cause a very good OPC model to function poorly is investigated. Our approach is to use two OPC models: a good OPC model and a weak OPC model. The setup parameters will be optimized for the weak OPC model to investigate any improvements in the overall OPC performance. Alternatively, setup parameters chosen poorly will be used with the good OPC model to see how this will adversely affect the OPC performance. A comparative study will be carried out in order to fully understand the effect of setup file parameters on the overall OPC performance. The general goal of this study is to help the OPC modelers and setup parameters optimizers to improve the quality and performance of the OPC solution and weigh the tradeoffs associated with different OPC solution choices.