Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
Abstract
Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 31, 2021
- Source ID
- 10.1038/s42005-021-00568-6
Entities
People
- Arka Majumdar
- Shane Colburn
Organizations
- National Science Foundation
- United States Department of Defense
- University of Washington
- Washington Research Foundation