Inverse design in nanophotonics

By S. Molesky, Zin Lin, Alexander Piggott, Weiliang Jin, Jelena Vuckovic, Alejandro W. Rodriguez. Nature Photonics, 2018

1,646 citations recorded by the source corpus.

Recent advancements in computational inverse-design approaches — algorithmic techniques for discovering optical structures based on desired functional characteristics — have begun to reshape the landscape of structures available to nanophotonics. Here, we outline a cross-section of key developments in this emerging field of photonic optimization: moving from a recap of foundational results to motivation of applications in nonlinear, topological, near-field and on-chip optics. Starting with a desired optical output it is possible to use computational algorithms to inverse design devices. The approach is reviewed here with an emphasis on nanophotonics.

Subfields: Atomic and Molecular Physics, and Optics, Biomedical Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/a2f2ae9a0b825d095f437e4ea7cfca5dad7d9386

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In Atomic and Molecular Physics, and Optics

In Biomedical Engineering

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Citations in the corpus

Physics-informed neural networks with hard constraints for inverse design (2021, methodology): “When the parameterization of the geometry is via a density function or level set so that the connectivity/topology is arbitrary, inverse de-sign is called topology optimization [34]; this is the variant of inverse design considered in this study.”
Global optimization of dielectric metasurfaces using a physics-driven neural network (2019, methodology): “These methods range from heuristic swarm 13 and genetic algorithms 14,15 to adjoint-based topology optimization, 16,17 and they have led to metagratings, 18,19 metasurfaces, 20–22 and other nanophotonic devices 23–25 with exceptional performance.”
Roadmap on spatiotemporal light fields (2022, background): “Why? For one, while the STML inverse problem is significantly more challenging than any problem attempted in inverse photonic design so far [18], it is not obviously harder than recent feats of data-driven models, such as the mastery of ImageNet, text generation, and the games of Go or Starcraft II ”