Skip to Main Content
Your organization might have access to this article on the publisher's site. To check, click on this link:http://dx.doi.org/+10.1116/1.3662407
In this work, we present a methodology for process window capable optical proximity correction (OPC) compact model building which requires only one nominal process condition empirical data for model calibration, and enables full and predictable extrapolation to any process condition within focus-exposure matrix. In order to ensure modeling success, a focus and dose balancing techniques are used during model calibration. The model optimization method is based on a stepwise fitting methodology where staged optimization of the OPC model components is used. Model components are added during the OPC model calibration starting with more physical components, such as mask and optics, followed by resist components. In each optimization stage, a component is optimized using global regression methods. The optimized parameters regressed in a small range about their optimal values during subsequent model component optimization. The effectiveness of this approach in terms of accurate correction, process window interpolation and extrapolation were compared with conventional fitting methods through computational experiments. Prediction of measured verification patterns were used to assess the calibrated model quality.