Zhang Qingyan, Junbo Liu, Sun Haifeng, Zhou Ji, Jin Chuan, Wang Jian, Li Yanli, Hu Song
Opt Express. 2024 Feb 12;32(4):5301-5322. doi: 10.1364/OE.515546.
Source and mask optimization (SMO) technology is increasingly relied upon for resolution enhancement of photolithography as critical dimension (CD) shrinks. In advanced CD technology nodes, little process variation can impose a huge impact on the fidelity of lithography. However, traditional source and mask optimization (SMO) methods only evaluate the imaging quality in the focal plane, neglecting the process window (PW) that reflects the robustness of the lithography process. PW includes depth of focus (DOF) and exposure latitude (EL), which are computationally intensive and unfriendly to gradient-based SMO algorithms. In this study, we propose what we believe to be a novel process window enhancement SMO method based on the Nondominated Sorting Genetic Algorithm II (NSGA-II), which is a multi-objective optimization algorithm that can provide multiple solutions. By employing the variational lithography model (VLIM), a fast focus-variation aerial image model, our method, NSGA-SMO, can directly optimize the PW performance and improve the robustness of SMO results while maintaining the in-focus image quality. Referring to the simulations of two typical patterns, NSGA-SMO showcases an improvement of more than 20% in terms of DOF and EL compared to conventional multi-objective SMO, and even four times superior to single-objective SMO for complicated patterns.
随着关键尺寸（CD）不断缩小，源掩模优化（SMO）技术在光刻分辨率增强方面的应用越来越广泛。在先进的CD技术节点中，微小的工艺变化都可能对光刻的保真度产生巨大影响。然而，传统的源掩模优化（SMO）方法仅评估焦平面上的成像质量，而忽略了反映光刻工艺稳健性的工艺窗口（PW）。工艺窗口包括焦深（DOF）和曝光宽容度（EL），它们的计算量很大，并且对基于梯度的SMO算法不友好。在本研究中，我们提出了一种基于非支配排序遗传算法II（NSGA-II）的工艺窗口增强型SMO新方法，NSGA-II是一种能够提供多个解决方案的多目标优化算法。通过采用变分光刻模型（VLIM），一种快速的聚焦变化空间图像模型，我们的方法NSGA-SMO能够在保持聚焦图像质量的同时，直接优化工艺窗口性能并提高SMO结果的稳健性。通过对两种典型图案的模拟，NSGA-SMO在焦深和曝光宽容度方面比传统的多目标SMO提高了20%以上，对于复杂图案甚至比单目标SMO高出四倍。
