We introduce WaveY-Net, a hybrid data-and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultrafast speeds and high accuracy for entire classes of dielectric nanophotonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: to calculate electric fields from the magnetic fields and as physical constraints in the loss function. We show that WaveY-Net can accurately predict the near-fields in periodic, high dielectric contrast nanostructure arrays, and that it can combine with gradientbased algorithms to dramatically accelerate the local and global freeform optimization of diffractive photonic devices by orders of magnitude faster speeds. We anticipate that physics-augmented deep neural networks will transform the practice of nanophotonics simulation and design.

We propose a three-dimensional freeform nanophotonic platform in which wavelength-scale domains comprise basic geometric structures with explicitly defined dimensions, positions, orientations, and minimum feature size constraints. Given a desired wavefront shaping objective, these parameters can be collectively optimized using gradient-based shape optimization with full accounting of near-field interactions between structures. We apply our concept to a variety of metagratings supporting high diffraction efficiencies and polarization control, and we experimentally demonstrate a device with a tailored polarization response as a function of wavelength. The combination of device capability, feature size constraints, and ease of manufacturability enabled by our methodology will facilitate the development of robust, high performance, nanophotonic technologies.

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Nanophotonic devices are optical platforms capable of unprecedented wavefront control. To push the limits of experimental device performance, scalable design methodologies that combine the simplicity and fabricability of conventional design paradigms with the extended capabilities of freeform optimization are required. We introduce a novel gradient-based design framework for large-area freeform metasurfaces in which nonlocal interactions between simply shaped nanostructures, placed on an irregular lattice, are tailored to produce high-order hybridized modes that support customizable large angle scattering profiles. Utilizing this approach, we design and experimentally demonstrate multifunctional super-dispersive metalenses. We also extend our approach to high numerical aperture radial metalenses capable of diffraction limited focusing and the generation of donut-shaped point spread functions. We anticipate that these concepts will have utility in super-resolution microscopy, particle trapping, additive manufacturing, and metrology applications that require ultra-high numerical apertures.