Journal article
W. Jia, Dajun Lin, R. Menon, B. Sensale‐Rodriguez
Optics Letters, 2023
Optics Letters, 2023
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APA
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Jia, W., Lin, D., Menon, R., & Sensale‐Rodriguez, B. (2023). Machine learning enables the design of a bidirectional focusing diffractive lens. Optics Letters.
Chicago/Turabian
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Jia, W., Dajun Lin, R. Menon, and B. Sensale‐Rodriguez. “Machine Learning Enables the Design of a Bidirectional Focusing Diffractive Lens.” Optics Letters (2023).
MLA
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Jia, W., et al. “Machine Learning Enables the Design of a Bidirectional Focusing Diffractive Lens.” Optics Letters, 2023.
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@article{w2023a,
title = {Machine learning enables the design of a bidirectional focusing diffractive lens.},
year = {2023},
journal = {Optics Letters},
author = {Jia, W. and Lin, Dajun and Menon, R. and Sensale‐Rodriguez, B.}
}
Abstract
Machine learning can efficiently empower the inverse design of cascaded diffractive optical elements. In this work, we explore the inverse design of a bidirectional focusing diffractive lens in a cascaded configuration through the diffractive optical neural network (DONN) machine learning method. The bidirectional focusing diffractive lens consists of two on-axially cascaded multi-level diffractive lenses. Each lens consists of concentric rings with equal widths and varying heights. The height of each concentric ring is optimized as part of the design algorithm. The diffractive lens has a focal length f+ as light propagates in the forward (Z+) direction. As light propagates in the backward (Z-) direction, the focal length changes to f-. The designed lens is fabricated through a two-photon polymerization 3D printing technique. The proposed design is polarization insensitive and miniature and can be readily applied in future functional optical imaging systems.