Abstract
Integrating conventional optics into compact nanostructured surfaces is the goal of flat-optics. Despite the enormous progress of this technology, there are still critical challenges for real world applications due to a limited operational efficiency in the visible, on average lower than 60%, which originates by absorption losses in wavelength thick (≈ 500 nm) structures. Another issue is the realization of on-demand optical components for controlling vectorial light at visible frequencies simultaneously in both reflection and transmission, and with a predetermined wavefront shape. In this work, we developed an inverse design approach that allows the realization of highly efficient (up to 99%) ultra-thin (down to 50 nm thick) optics for vectorial light control with broadband input-output responses in the visible and near IR on a desired wavefront shape. The approach leverages on suitably engineered semiconductor nanostructures, which behave as a neural network that can approximate a user defined input-output function. Near unity performance results from the ultra-thin nature of these surfaces, which reduces absorption losses to near negligible values. Experimentally, we discuss polarizing beam splitters, comparing their performance with the best results obtained from both direct and inverse design techniques, and new flat-optics components represented by dichroic mirrors and the basic unit of a flat optics display that creates full colors by using only two sub-pixels, overcoming the limitations of conventional LCD/OLED technologies that require three sub-pixels for each composite color. Our devices can be manufactured with a complementary metal-oxide-semiconductor (CMOS) compatible process, making them scalable for mass production at inexpensive costs.
Original language | English (US) |
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Journal | Light Science and Applications |
State | Published - Dec 14 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2021-01-08Acknowledged KAUST grant number(s): OSR-2016-CRG5-2995
Acknowledgements: This research received funding from KAUST (Award OSR-2016-CRG5-2995). Parallel simulations are performed on KAUST’s Shaheen supercomputer.