Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Optimization technologies have been widely applied to improve lithography performance, such as optical proximity correction and source mask optimization (SMO). However, most published optimization technologies were performed under fixed process conditions, and only a few parameters were optimized. A method for mask, process, and lithography-tool parameter co-optimization (MPLCO) is developed to extend the process window. A normalized conjugate gradient algorithm is proposed to improve the convergence efficiency of the MPLCO when optimizing different scale parameters. In addition, a parametric mask and source are used in the MPLCO that could obtain exceedingly low mask and source complexity compared with a traditional SMO.