Deep neural networks for inverse design of multimode integrated gratings with simultaneous amplitude and phase control
Ali Mohajer Hejazi, Vincent Ginis
TL;DR
This paper introduces a method using deep neural networks to design photonic gratings that control both amplitude and phase of reflected light.
Contribution
A novel inverse design approach using deep learning for multimode photonic gratings with simultaneous amplitude and phase control.
Findings
Neural networks were trained to map grating parameters to scattering parameter magnitudes and phases.
Inverse design enabled precise control over reflected modes for advanced nanophotonic applications.
The method allows for creating complex wave interference patterns in optical systems.
Abstract
We present a photonic mode converter based on a grating structure, modeled and inversely designed by deep neural networks. The neural network maps the physical parameters of the grating to the grating responses, i.e., complex scattering parameters representing the reflected modes from the grating structure. We design different neural networks to output the magnitudes and the phases of the scattering parameters associated with the multiple reflected modes. Following the training process, we use the trained networks to perform inverse design of the grating based on the desired magnitudes of the scattering parameters. The inverse design effort provides a full control on the magnitudes and the phases of the reflected modes from the mode converter. Our techniques help in creating a rich landscape of multiple interfering waves that provide higher control on optical near fields, complex…
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Taxonomy
TopicsPhotonic and Optical Devices · Advanced Fiber Optic Sensors · Optical Coatings and Gratings