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Multifocal multilevel diffractive lens by wavelength multiplexing.

Wei Jia, Dajun Lin, Rajesh Menon

|September 14, 2023

Summary

This summary is machine-generated.

Machine learning inverse designs a multifocal multilevel diffractive lens (MMDL) for integrated imaging. This tunable flat lens achieves varied focal lengths by wavelength multiplexing across red, green, and blue light.

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Area of Science:

Optics and Photonics
Machine Learning Applications
Nanotechnology

Background:

Flat lenses offer miniaturization potential for advanced imaging systems.
Tunable focal length is crucial for integrated optical devices.
Diffractive lenses typically exhibit wavelength-dependent focal lengths.

Purpose of the Study:

To inverse design a multifocal multilevel diffractive lens (MMDL) using machine learning.
To achieve wavelength-multiplexed focal lengths for red, green, and blue light.
To demonstrate a tunable, miniature, and polarization-insensitive flat lens.

Main Methods:

Machine learning was employed for inverse design of the MMDL.
The MMDL structure consists of concentric rings with optimized heights.
Fabrication was performed using direct-write laser lithography with gray-scale exposure.

Main Results:

The MMDL achieved distinct focal lengths of 4 mm (red), 20 mm (green), and 40 mm (blue).
Focal lengths exhibited significant wavelength dependency, a key design feature.
The fabricated lens was miniature and polarization insensitive.

Conclusions:

The developed MMDL demonstrates effective wavelength-multiplexed multifocal capabilities.
Machine learning provides a powerful tool for designing complex optical elements like MMDLs.
This technology holds promise for integrated optical imaging systems, including zooming functionalities.