Abstract

Dispersion control is a critical aspect in nano-optical systems. Moreover, chromatic aberration significantly impacts image quality. Despite metasurfaces being a novel approach to tackle chromatic aberration in diffractive lenses, numerous challenges hinder their practical implementation due to the complexity of 3D fabrication techniques and high manufacturing costs. In contrast, ultra-thin graphene oxide lenses are simpler and less expensive to manufacture. The optical performance of graphene oxide lenses, such as high focusing efficiency, large depth of field, wide bandwidth, and zooming capability, depends on the design of the positional arrangement of reduced graphene oxide regions. In this study, we utilized the self-constructed datasets to train machine learning models based on the structure of the graphene oxide lens and combined it with intelligent optimization algorithms. This approach facilitated the design of the graphene oxide achromatic lens in multi-wavelengths with high-performance. Experimental results substantiate that the designed ultra-thin graphene oxide lens, with a thickness of ~200 nm, effectively controls dispersion across multiple incident wavelengths (450, 550, and 650 nm) and achieves super resolution with consistent intensity at the focal point. Our graphene oxide lens holds the potential for integration into micro-optical systems that demand dispersion control, providing broad applications in optical imaging, optical communication, the biomedical field, and beyond.

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