Deep learning–based moiré-fringe alignment with circular gratings for lithography

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.