Zhao Rongbo, Dong Lisong, Chen Rui, Wei Yayi
Appl Opt. 2021 Feb 10;60(5):1341-1348. doi: 10.1364/AO.417093.
Extreme ultraviolet lithography (EUVL) presents promise for the advanced technology node in the manufacturing of integrated circuits. The imaging performance of EUVL is significantly affected by the aberration of projection optics. To obtain one optimum aberration for different test patterns, an inverse optimization method for aberration is proposed in this paper. The aberration models of three types of test patterns are first established by applying the backpropagation (BP) neural network. Then choosing the common indicators of the lithography process variation band (PVB) and pattern shift (PS) as the objective function, an aberration optimization method based on the algorithm of simulated annealing is proposed. After applying the optimization method, a set of optimized aberrations and the corresponding PVBs and PSs are obtained and analyzed. These results are finally compared with those from rigorous simulations. The comparison results show that zero aberration is non-optimal distribution in EUVL image simulation with mask topography. In addition, the high prediction accuracy and robustness of aberration optimization is also demonstrated from the results.
极紫外光刻技术（EUVL）在集成电路制造的先进技术节点方面展现出了前景。EUVL的成像性能受到投影光学系统像差的显著影响。为了针对不同测试图案获得最佳像差，本文提出了一种像差的逆优化方法。首先通过应用反向传播（BP）神经网络建立三种测试图案的像差模型。然后选择光刻工艺变化带（PVB）和图案偏移（PS）的常见指标作为目标函数，提出一种基于模拟退火算法的像差优化方法。应用该优化方法后，获得并分析了一组优化后的像差以及相应的PVB和PS。最后将这些结果与严格模拟的结果进行比较。比较结果表明，在具有掩模形貌的EUVL图像模拟中，零像差并非最优分布。此外，结果还证明了像差优化具有较高的预测精度和鲁棒性。
