Decomposition-learning-based thick-mask model for partially coherent lithography system

Abstract

The simulation of thick-mask diffraction near-field (DNF) is an indispensable process in aerial image calculation of immersion lithography. In practical lithography tools, the partially coherent illumination (PCI) is applied since it can improve the pattern fidelity. Therefore, it is necessary to precisely simulate the DNFs under PCI. In this paper, a learning-based thick-mask model proposed in our previous work is extended from the coherent illumination condition to PCI condition. The training library of DNF under oblique illumination is established based on the rigorous electromagnetic field (EMF) simulator. The simulation accuracy of the proposed model is also analyzed based on the mask patterns with different critical dimensions (CD). The proposed thick-mask model is shown to obtain high-precise DNF simulation results under PCI, and thus is suitable for 14 nm or larger technology nodes. Meanwhile, the computational efficiency of the proposed model is improved up to two orders of magnitude compared to the EMF simulator.