Fourier-attention network: A deep neural network for lithographic misalignment sensing

A recurring challenge in integrated lithography is subnanoscale misalignment sensing. In widely-used moire'-based misalignment sensing schemes, measurement accuracy is restricted by the performance of the image processing schemes. This is also a fundamental problem in the field of Fourier optics that has received extensive attention in the science and engineering fields. This paper proposes a Fourier-attention neural network that can achieve real time-lapse misalignment sensing with an accuracy of 0.23 nm. This is enabled by the system's robustness to system errors and noise. We hope that this strategy can provide an effective solution for various misalignment sensing applications and that the approach can be applied to future problems.