An advanced modeling approach for mask and wafer process simulation

A new modeling technique to accurately represent the mask and wafer process behavior is presented. The lithography simulation can be done in three steps: i) mask simulation, ii) latent image calculations and iii) resist process simulation. The leading edge designs, such as 32 nm and beyond, require higher-fidelity models to adequately represent each of these actual processes. Effects previously considered secondary, have become more pronounced with each new technology node. In this approach, we utilized separate physical models for both mask and wafer processes. We demonstrate that the residual errors can be further reduced when nonlinear mappers are used in addition. The advantage of the presented approach compared to standard curve-fitting or statistics-based models is its predictive power and adaptive nature. The physical model parameters were calibrated by a genetic algorithm whose details were outlined in [1]. The nonlinear mapper model parameters were identified by a gradient descent method. Given the computational requirements for a practical solution, our approach uses graphics processors as well as CPUs as computation hardware.