Machine learning techniques using artificial neural networks (ANN) have proven to be extremely ef-fective in designing nanophotonic systems. This presentation focuses on two applications where ANNs are utilized for designing nanophotonic scatterers.
In the first scenario, ANNs act as surrogate solvers for Maxwell's equations, allowing the design of scatterers tailored to specific fabrication technologies like laser nanoprinting. Designing low-index material scatterers is complex, so solving the inverse problem multiple times from different starting points is crucial. A Fourier neural operator ANN serves as a surrogate Maxwell solver, simplifying this process.
The second scenario integrates ANNs into a holistic metasurface design framework. Individual meta-atoms are efficiently described by their scattering responses, typically expressed as polarizability or T-matrix that provide metasurfaces with functionality on demand. Then, suitably trained ANNs are used to identify feasible physical objects that offer the desired T-matrices.
We use convolutional neural networks (CNN) to predict scattering geometry from the fields outside of the scatterer. While this problem is nonunique, we show that by training on specific datasets, the CNN learns the underlying structure of the scatterers. I.e., if there is prior knowledge of the expected structure or form of the scatterers, this can be used to obtain a much more accurate solution to the inverse scattering problem. We show that our method faithfully recovers the original geometry for highly specific classes of structures, while the more conventional method falls victim to the nonuniqueness and fails to recover plausible-looking geometries.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.