Multi-objective adaptive source optimization for full chip

Abstract

Source optimization (SO) is an extensively used resolution enhancement technique in optical lithography. To improve computational efficiency, compressive sensing (CS) theory was applied to SO for clip-level applications in previous works. We propose, for the first time to our knowledge, a multi-objective adaptive SO (adaptive-MOSO) with CS for full chip. The fast optimization of a pixel illumination source pattern is achieved, and the imaging fidelity of each clip is guaranteed simultaneously at full chip. Fast CS with contour sampling is applied to accelerate the SO procedure by sampling all layout patterns. Novel cost function with adaptive weight distribution for every single clip is established to guarantee the lithography imaging fidelity for full chip. The simulation results prove that the adaptive-MOSO method improves the efficiency of SO and the lithography performance for large-scale chips.

Source optimization based on compression sensing for plasmonic lithography

“…Due to the efficient and fast characteristics of the linear Bergman algorithm, in this study, we used this algorithm to optimize formula (6). The accuracy of CS reconstruction signals depends on two conditions: sparsity and non correlation [8][9][10]. The sparser  , the more uncorrelated the matrices  and  , the higher the accuracy of CS reconstruction signal.…”

“…Through joint training of projection matrix and sparse basis, the imaging fidelity and computational efficiency were effectively improved. Subsequently, Lin et al [9] analyzed and compared the impact of different types of sparse basis matrices on the optimization results of CSSO. This paper is based on plasma imaging lithography, which optimizes the existing source modes into pixelate sources.…”