Efficient mask synthesis with augmented Lagrangian methods in computational lithography

Abstract

A fast approach based on augmented Lagrangian methods (ALMs) is proposed to solve the inverse imaging problem in optical lithography, known as inverse lithography technology. We develop a constrained optimization framework where the objective function includes a data-fidelity term and a binary equality constraint. We show how optimal solutions are reached with less execution time by applying the quasi-Newton method to the sub-problem. The proposed scheme also includes a tentative penalty parameter schedule for adjustment and control. Simulation results are compared with existing source-mask optimization (SMO) to illustrate the performance improvement in terms of pattern fidelity, convergence rate and process window size. Copyright © 2013 by ECS - The Electrochemical Society.