Model-based optical proximity correction for immersion lithography-based flat optics platform

Abstract

We introduce well-developed optical proximity correction (OPC) techniques to the metasurface-based flat optics manufacturing process. Flat optics, formed by subwavelength scale nanostructure pillar (nanopillar) array, so called metasurface, has become promising substitutes for conventional bulky optical components. For its manufacturing, photolithography is preferable rather than the electron beam lithography (EBL) technique because of its time and cost effectiveness for mass manufacturing. However, the required feature size and pitch of the metasurface for the visible light is approaching the process limit of the ArF immersion lithography. It results in critical dimension (CD) errors due to optical proximity effect and could result in efficiency degradation of the flat optics. In the semiconductor manufacturing industry, OPC based on process modelling and numerical computation has been developed for the last few decades to control the CD on the wafer. Here, a machine learning (ML) model is constructed to avoid the time consumption of the conventional OPC method without losing the accuracy. Various pitches of flat optics metalens, from 465 nm to 160 nm, has been studied for the implementation of the ML OPC. The root mean square (RMS) CD errors < 1 nm and the CD accuracies < 6 nm can be achieved. The CD error percentages over the pillar diameters < 6 % is observed and the improvement of CD error and CD accuracy compared to rule based OPC in small pitches of metalens is demonstrated.