Probabilistic inverse design of metasurfaces using mixture density neural networks
Year: 2024
Type: article
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.
Source: Journal of Physics Photonics
Authors: Mahsa Torfeh, Chia Wei Hsu
Institution: University of Southern California
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,470
Funder: National Science Foundation
OpenAlex ID: W4405133080