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Abstract Metasurfaces are planar sub-micron structures that can outperform traditional optical elements and miniaturize optical devices. Optimization-based inverse designs of metasurfaces often get trapped in a local minimum, and the inherent non-uniqueness property of the inverse problem plagues approaches based on conventional neural networks. Here, we use mixture density neural networks to overcome the non-uniqueness issue for the design of metasurfaces. Once trained, the mixture density network (MDN) can predict a probability distribution of different optimal structures given any desired property as the input, without resorting to an iterative local optimization. As an example, we use the MDN to design metasurfaces that project structured light patterns with varying fields of view. This approach enables an efficient and reliable inverse design of fabrication-ready metasurfaces with complex functionalities without getting trapped in local optima.
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Controlling three-dimensional optical fields via inverse Mie scattering
Controlling the propagation of optical fields in three dimensions using arrays of discrete dielectric scatterers is an active area of research. These arrays can create optical elements with functionalities unrealizable in conventional optics. Here, we present an inverse design method based on the inverse Mie scattering problem for producing three-dimensional optical field patterns. Using this method, we demonstrate a device that focuses 1.55-μm light into a depth-variant discrete helical pattern. The reported device is fabricated using two-photon lithography and has a footprint of 144 μm by 144 μm, the largest of any inverse-designed photonic structure to date. This inverse design method constitutes an important step toward designer free-space optics, where unique optical elements are produced for user-specified functionalities.
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- Award ID(s):
- 1825308
- PAR ID:
- 10189292
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 5
- Issue:
- 10
- ISSN:
- 2375-2548
- Page Range / eLocation ID:
- eaax4769
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1126/sciadv.aax4769
