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Register a new userInverse design of optical elements based on arrays of dielectric spheres
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Arrays of wavelength scale scatterers are a promising platform for designing optical elements with a compact footprint. The large number of degrees of freedom in this system allows for unique and plentiful functionalities. However, the many variables also create a complex design problem. While intuitive forward design methods work for simple optical elements, they often fail to produce complicated elements, especially those involving multiple elements. We present an inverse design methodology for large arrays of wavelength scale spheres based on both adjoint optimization or sensitivity analysis and generalized multi-sphere Mie theory as a solution to the design problem. We validate our methodology by designing two sets of optical elements with scatterers on sub-wavelength and super-wavelength periodicity grids. Both sets consist of a singlet and a doublet lens, with one and two layers of spheres respectively designed for 1550 nm. The designed NA is ~0.33 (~0.5) for the sub-wavelength (super-wavelength) periodic structure. We find that with the sub-wavelength periodicity, the full width at half maximum of the focal spot produced by the singlet and doublet is smaller than that produced by an ideal lens with the same geometric parameters. Finally, we simulate a realistic experimental scenario for the doublet where the spheres are placed on a substrate with the same refractive index. We find the performance is similar, but with lower intensity at the focal spot and larger spot size. The method described here will simplify the design procedure for complicated multifunctional optical elements and or scatterer array-based volume optics based on a specified figure of merit.
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The exact suppression of backscattering from rotationally symmetric objects requires dual symmetric materials where ${epsilon_r} = {mu_r}$. This prevents their design at many frequency bands, including the optical one, because magnetic materials are not available. Electromagnetically small non-magnetic spheres of large permittivity offer an alternative. They can be tailored to exhibit balanced electric and magnetic dipole polarizabilities, which result in approximate zero backscattering. In this case, the effect is inherently narrowband. Here, we put forward a different alternative that allows broadband functionality: Electromagnetically large spheres made from low permittivity materials. The effect occurs in a parameter regime that approaches the trivial ${epsilon_r} to {mu_r} =1$ case, where approximate duality is met in a weakly wavelength dependence fashion. Despite the low permittivity, the overall scattering response of the spheres is still significant. Radiation patterns from these spheres are shown to be highly directive across an octave spanning band. The effect is analytically and numerically shown using the Mie coefficients.
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Nanocomposites with tailored optical properties can provide a new degree of freedom for optical design. However, despite their potential these materials remain unused in bulk applications. Here we investigate the conditions under which they can be used for optical applications using Mie theory, effective medium theories, and numerical simulations based on the finite element method. We show that due to scattering different effective medium regimes have to be distinguished, and that bulk materials can only be realized in a specific parameter range. Our analysis also enables us to quantify the range of validity of different effective medium theories, and identify design rules on how the free material parameters should be adjusted for specific applications.
Optical materials with special optical properties are widely used in a broad span of technologies, from computer displays to solar energy utilization leading to large dataset accumulated from years of extensive materials synthesis and optical characterization. Previously, machine learning models have been developed to predict the optical absorption spectrum from a materials characterization image or vice versa. Herein we propose TLOpt, a transfer learning based inverse optical materials design algorithm for suggesting material compositions with a desired target light absorption spectrum. Our approach is based on the combination of a deep neural network model and global optimization algorithms including a genetic algorithm and Bayesian optimization. A transfer learning strategy is employed to solve the small dataset issue in training the neural network predictor of optical absorption spectrum using the Magpie materials composition descriptor. Our extensive experiments show that our algorithm can inverse design the materials composition with stoichiometry with high accuracy.
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