Wang Nan, Jiang Wei, Zhang Yu
Opt Lett. 2021 Mar 1;46(5):1113-1116. doi: 10.1364/OL.414617.
In lithography, misalignment measurement with a large range and high precision in two dimensions for the overlay is a fundamental but challenging problem. For moiré-based misalignment measurement schemes, one potential solution is considered to be the use of circular gratings, whose formed moiré fringes are symmetric, isotropic, and aperiodic. However, due to the absence of proper analytical arithmetic, the measurement accuracy of such schemes is in the tens of nanometers, resulting in their application being limited to only coarse alignments. To cope with this problem, we propose a novel deep learning-based misalignment measurement strategy inspired by deep convolutional neural networks. The experimental results show that the proposed scheme can achieve nanoscale accuracy with micron-scale circular alignment marks. Relative to the existing strategies, this strategy has much higher precision in misalignment measurement and much better robustness to fabrication defects and random noise. This enables a one-step two-dimensional nanoscale alignment scheme for proximity, x-ray, extreme ultraviolet, projective, and nanoimprint lithographies.
在光刻技术中，对套刻进行二维大范围内高精度的对准误差测量是一个基本但具有挑战性的问题。对于基于莫尔条纹的对准误差测量方案，一种潜在的解决方案被认为是使用圆形光栅，其形成的莫尔条纹是对称、各向同性且非周期性的。然而，由于缺乏合适的解析算法，此类方案的测量精度在几十纳米，导致其应用仅限于粗对准。为了解决这个问题，我们受深度卷积神经网络启发，提出了一种基于深度学习的新型对准误差测量策略。实验结果表明，所提出的方案使用微米级圆形对准标记即可实现纳米级精度。相对于现有策略，该策略在对准误差测量方面具有更高的精度，对制造缺陷和随机噪声具有更好的鲁棒性。这使得能够为接近式光刻、X射线光刻、极紫外光刻、投影光刻和纳米压印光刻实现一步二维纳米级对准方案。
