I-line photolithographic metalenses enabled by distributed optical proximity correction with a deep-learning model.

High pattern fidelity is paramount to the performance of metalenses and metasurfaces, but is difficult to achieve using economic photolithography technologies due to low resolutions and limited process windows of diverse subwavelength structures. These hurdles can be overcome by photomask sizing or reshaping, also known as optical proximity correction (OPC). However, the lithographic simulators critical to model-based OPC require precise calibration and have not yet been specifically developed for metasurface patterning. Here, we demonstrate an accurate lithographic model based on Hopkin's image formulation and fully convolutional networks (FCN) to control the critical dimension (CD) patterning of a near-infrared (NIR) metalens through a distributed OPC flow using i-line photolithography. The lithographic model achieves an average ΔCD/CD = 1.69% due to process variations. The model-based OPC successfully produces the 260 nm CD in a metalens layout, which corresponds to a lithographic constant k1 of 0.46 and is primarily limited by the resolution of the photoresist. Consequently, our fabricated NIR metalens with a diameter of 1.5 mm and numerical aperture (NA) of 0.45 achieves a measured focusing efficiency of 64%, which is close to the calculated value of 69% and among the highest reported values using i-line photolithography.