Comparison of different lithographic source optimization methods based on compressive sensing

Source optimization (SO) is a widely used resolution enhancement technique to improve the imaging performance of optical lithography systems. Recently, a fast pixelated SO method for inverse lithography has been developed based on the theory of compressive sensing (CS). In last several years, CS has explored numerous reconstruction algorithms to solve for inverse problems. These algorithms are critical in attaining good reconstruction quality also aiming at reducing the time complexity. This paper compares different SO methods based on CS algorithms including the linearized Bregman (LB) algorithm, the alternating direction method of multipliers (ADMM), the fast iterative shrinkage-thresholding algorithm (FISTA), the approximate message-passing (AMP), and the gradient projection for sparse reconstruction (GPSR). Benefiting from the strategy of variable splitting and adaptive step size searching, the GPSR method effectively retains the optimization efficiency. Computational experiments also show that the GPSR method can achieve superior or comparable SO performance on average over other methods. It is also shown that the proposed SO methods can be applied to develop a fast source-mask optimization (SMO) method based on the CS framework.