Source optimization based on compression sensing for plasmonic lithography

Abstract

Plasmonic lithography can amplify the evanescent wave resonance at the mask and participate in imaging by exciting surface plasmon polaritons (SPPs), breaking the diffraction limit in traditional lithography. Source Optimization (SO) technology is widely used to compensate for imaging distortion in traditional lithography. This paper proposes an effective SO model for plasmonic lithography under the compressed sensing (CS) framework. To accelerate the algorithm, the SO is formulated as an underdetermined linear problem, where the number of equations is much smaller than the source variables. We selected lines, contacts, and complex test patterns to verify the imaging improvements and superiority of the model. The results indicate that compared to the annular sources, optimized sources can achieve better imaging results and higher imaging contrast. This provides favorable conditions for the large-scale application of plasmonic lithography.