Effect of mask 3D and scanner focus difference on OPC modeling and verification

A robust optical proximity correction (OPC) model must include process variation to be effective in volume manufacturing. Often, calibration of an OPC model is based on data from a single scanner. However, scanner and mask three dimension (3D) effects have been found to affect printing performance and OPC model effectiveness [1]. OPC model robustness is improved if the fingerprints of different scanners are matched as closely as possible. Scanner source map or boundary condition variations can cause isolated and dense feature focus differences between different scanners. The scanner used to build a robust OPC model should have a minimum focus difference between isolated and dense features. Mask 3D effects must be included in OPC model building. Even if the design data is the same, mask 3D effects will vary by different advanced blank film stacks and model fitting will lead to different results. In this work, the effects of focus differences between nested and isolated features for OPC model building are quantified. In addition, mask 3D effect contributions to OPC models will also be illustrated. OPC model tolerance to variation is shown using data from multiple scanners and mask topographies and methodologies to optimize OPC models are presented. The data confirms that different absorber thickness, and n and k values, for advanced binary masks will influence the boundary conditions and effect lithographic performance. A thinner absorber demonstrated better CD prediction than thicker blanks in semi-dense and isolated patterns for both CDTP and inverse CDTP. It also shows that the thinner absorber has better inverse linearity in small isolated features, and has much better prediction for large isolated patterns. The generation of OPC models must include variations due to mask material properties and scanner optical variations to provide robust performance in manufacturing.