Abstract. Background: To ensure consistent and high-quality semiconductor production at future logic nodes, additional metrology tools are needed. For this purpose, grazing-incidence small-angle x-ray scattering (GISAXS) is being considered because measurements are fast with a proven capability to reconstruct average grating line profiles with high accuracy. Aim: GISAXS measurements of grating line shapes should be extended to samples with pitches smaller than 50 nm and their defects. The method’s performance should be evaluated. Approach: A series of gratings with 32-nm pitch and deliberately introduced pitchwalk is measured using GISAXS. The grating line profiles with associated uncertainties are reconstructed using a Maxwell solver and Markov-chain Monte Carlo sampling combined with a simulation library approach. Results: The line shape and the pitchwalk are generally in agreement with previously published transmission small-angle x-ray scattering (SAXS) results. However, the line height and line width show deviations of   (  1.0  ±  0.2  )    nm and   (  2.0  ±  0.7  )    nm, respectively. The complex data evaluation leads to relatively high pitchwalk uncertainties between 0.5 and 2 nm. Conclusions: GISAXS shows great potential as a metrology tool for small-pitch line gratings with complex line profiles. Faster simulation methods would enable more accurate results.

中文翻译：

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使用掠入射小角 X 射线散射提取自对准多重图案化生产的光栅的尺寸参数

摘要。背景：为了确保未来逻辑节点的一致和高质量的半导体生产，需要额外的计量工具。为此，正在考虑掠入射小角度 X 射线散射 (GISAXS)，因为测量速度快，并且已证明能够以高精度重建平均光栅线轮廓。目标：光栅线形状的 GISAXS 测量应扩展到间距小于 50 nm 的样品及其缺陷。应评估该方法的性能。方法：使用 GISAXS 测量一系列间距为 32 纳米的光栅和特意引入的间距步长。使用 Maxwell 求解器和 Markov-chain Monte Carlo 采样结合仿真库方法重建具有相关不确定性的光栅线轮廓。结果：线形和坡度步道通常与先前公布的透射小角 X 射线散射 (SAXS) 结果一致。然而，线高和线宽分别显示出 (1.0 ± 0.2) nm 和 (2.0 ± 0.7) nm 的偏差。复杂的数据评估导致 0.5 和 2 nm 之间的相对较高的音高步走不确定性。结论：GISAXS 显示出作为具有复杂线轮廓的小间距线光栅的计量工具的巨大潜力。更快的模拟方法将获得更准确的结果。复杂的数据评估导致 0.5 和 2 nm 之间的相对较高的音高步走不确定性。结论：GISAXS 显示出作为具有复杂线轮廓的小间距线光栅的计量工具的巨大潜力。更快的模拟方法将获得更准确的结果。复杂的数据评估导致 0.5 和 2 nm 之间的相对较高的音高步走不确定性。结论：GISAXS 显示出作为具有复杂线轮廓的小间距线光栅的计量工具的巨大潜力。更快的模拟方法将获得更准确的结果。