A deep learning aided optimization algorithm for the design of flat thin-film multilayer optical systems is developed. The authors introduce a deep generative neural network, based on a variational autoencoder, to perform the optimization of photonic devices. This algorithm allows one to find a near-optimal solution to the inverse design problem of creating an anti-reflective grating, a fundamental problem in material science. As a proof of concept, the authors demonstrate the method’s capabilities for designing an anti-reflective flat thin-film stack consisting of multiple material types. We designed and constructed a dielectric stack on silicon that exhibits an average reflection of 1.52 %, which is lower than other recently published experiments in the engineering and physics literature. In addition to its superior performance, the computational cost of our algorithm based on the deep generative model is much lower than traditional nonlinear optimization algorithms. These results demonstrate that advanced concepts in deep learning can drive the capabilities of inverse design algorithms for photonics. In addition, the authors develop an accurate regression model using deep active learning to predict the total reflectivity for a given optical system. The surrogate model of the governing partial differential equations can then be broadly used in the design of optical systems and to rapidly evaluate their behavior.
An efficient photovoltaic power converter is a critical element in laser power beaming systems for maximizing the end-to-end power transfer efficiency while minimizing beam reflections from the receiver for safety considerations. We designed a multilayer absorber that can efficiently trap monochromatic light from broad incident angles. The proposed design is built on the concept of a one-way coherent absorber with inverse-designed aperiodic multilayer front- and back-reflectors that enable maximal optical absorption in a thin-film photovoltaic material for broad angles. We argue that the broad bandwidth is achieved through an optimization search process that automatically engineers the modal content of the cavity to create multiple overlapping resonant modes at the desired angle or frequency range. A realistic design is provided based on GaAs thin films with inverse-designed multilayer binary AlAs/AlGaAs mirrors. The proposed device can pave the way for efficient optical power beaming systems.
more » « less- Award ID(s):
- 2112550
- NSF-PAR ID:
- 10440004
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 31
- Issue:
- 17
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 28285
- Size(s):
- Article No. 28285
- Sponsoring Org:
- National Science Foundation
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