Open AccessOptimization of lithography source illumination arrays using diffraction subspaces
Author(s) -
Xu Ma,
Zhiqiang Wang,
Haijun Lin,
Yanqiu Li,
Gonzalo R. Arce,
Lu Zhang
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.003738
Subject(s) - subspace topology , computation , linear subspace , robustness (evolution) , algorithm , computer science , pixel , norm (philosophy) , optics , mathematics , artificial intelligence , physics , biochemistry , chemistry , geometry , political science , law , gene
An efficient and robust lithography illumination optimization (ILO) approach is developed based on subspace compressive sensing (CS) and an lp-norm reconstruction algorithm. Instead of optimizing the source pattern over all its degrees of freedom, the proposed method only optimizes the source pixels in a subspace. The subspace includes the source pixels inducing interference between different diffraction orders of the mask pattern. The ILO is then formulated as an lp-norm (0 < p < 1) inverse reconstruction problem under the sparse representation of the source pattern. The subspace CS method benefits from having a significantly smaller number of optimization variables, thus effectively improving the computation speed. In addition, an lp-norm reconstruction algorithm is used, which is more robust than l 1 -norm reconstruction algorithms. Based on the simulations at 45nm and 14nm technology nodes, the proposed methods prove to improve the computational efficiency, robustness and imaging performance of current ILO methods based on adaptive CS.