With the increasing requirement of lithographic resolution, the degradation of 3D mask effect on imaging cannot be ignored. The researches of its polarization properties and effect on imaging are of great significance to the development of imaging-based aberration measurement techniques and computational lithography. In this paper, a novel method for comprehensive and quantitative characterization of 3D mask effect is proposed. By comparing the far-field spectrum of Kirchhoff model and 3D mask model, the 3D mask effect is comprehensively and quantitatively characterized as the form of polarization aberration. Pupil-spectrum comprehensive analysis method and background glitch noise culling method are proposed to improve the systematicness and accuracy of 3D mask characterization. The simulation comprehensively analyzes the effect of mask line width and absorber thickness on all polarization properties of the 3D mask effect, showing that this method can provide a more comprehensive analysis of the 3D mask effect compared with the previous methods.
Fast source pupil optimization (SO) has appeared as an important technique for improving lithographic imaging fidelity and process window (PW) in holistic lithography at 7-5nm node. Gradient-based methods are generally used in current SO. However, most of these methods are time-consuming. In our previous work, compressive sensing (CS) theory is applied to accelerate the SO procedure, where the SO is formulated as an underdetermined linear problem by randomly sampling monitoring pixels on mask features. CS-SO theory assumes that the source pattern is a sparse pattern on a certain basis, then the SO is transformed into a L1-norm or Lp-norm (0<p<1) image reconstruction problem. However, above methods are relaxation approaches of L0-norm method for convenient achievement. In this paper, to our best knowledge, transformed L1 penalty (TL1) and the difference of convex functions algorithm (DCA) for TL1 (DCATL1) are first developed to solve this inverse lithography SO problem in advantages. The source pattern is optimized by minimizing cost function pattern error with TL1 penalty. The DCATL1 method decomposes this cost function into the difference of two convex functions. By linearizing one convex function, the SO procedure can be transformed into a sequence of strongly convex minimization sub-problems, which can be accurately and efficiently solved by the Fast Alternating Direction Method of Multipliers (Fast ADMM) algorithm. Compared to previous methods, DCATL1 method can simultaneous realize fast and robust SO.
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