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Li ZQ, Dong LS, Ma X et al. Fast source mask co-optimization method for high-NA EUV lithography. Opto-Electron Adv 7, 230235 (2024). doi: 10.29026/oea.2024.230235
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Fast source mask co-optimization method for high-NA EUV lithography
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Abstract
Extreme ultraviolet (EUV) lithography with high numerical aperture (NA) is a future technology to manufacture the integrated circuit in sub-nanometer dimension. Meanwhile, source mask co-optimization (SMO) is an extensively used approach for advanced lithography process beyond 28 nm technology node. This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system. A fast high-NA EUV lithography imaging model is established first, which includes the effects of mask three-dimensional structure and anamorphic magnification. Then, this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressive-sensing-based source optimization algorithm. A mask rule check (MRC) process is further proposed to simplify the optimized mask pattern. Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error, and maintain high computational efficiency. -
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References
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Li ZQ, Dong LS, Ma X et al. Fast source mask co-optimization method for high-NA EUV lithography. Opto-Electron Adv 7, 230235 (2024). doi: 10.29026/oea.2024.230235
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Figure 1.
Sketches of the projection systems for the (a) 1.35NA DUV immersion lithography, (b) 0.33NA EUV lithography, and (c) 0.55NA EUV lithography.
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Figure 2.
The flows of (a) the lithography process, and (b) the proposed imaging model.
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Figure 3.
The illustrations of (a) the source plane, and (b) the impacts of central obstruction in the projection system.
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Figure 4.
The flow of the thick-mask model. (a) The L-shape mask pattern. (b) The mask decomposition. (c) The DNF segments calculated based on convolution kernels. (d) The final combined DNF for the entire mask pattern.
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Figure 5.
The comparison of the proposed imaging model and a commercial software. (a) The aerial images of the mask patterns with 13 nm CD and 9 nm CD that are calculated by the proposed model (left) and commercial software (right), and (b) the cross sections of the normalized aerial images along the red line.
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Figure 6.
The flowcharts of (a) the SO algorithm, (b) the main SMO method, and (c) the MO algorithm.
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Figure 7.
The Illustration of (a) the seven testing layout patterns, and (b) scaling down of a mask pattern.
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Figure 8.
The optimization results obtained by the proposed SMO method. (a) and (b) show the results of pattern #1 and pattern #2 with 13 nm CD.
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Figure 9.
The optimization results obtained by the proposed SMO method. (a) and (b) show the results of pattern #1 and pattern #2 with 9 nm CD.
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Figure 10.
The optimization results obtained by the proposed SMO method. (a) and (b) show the results of pattern #1 and pattern #2 with 7 nm CD.
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Figure 11.
Results of the MRC. (a) The target mask and the illumination condition. (b) The optimized mask pattern and its resist pattern. (c) The mask after MRC and its resist pattern.
