Computational inverse design for cascaded systems of metasurface optics

Abstract

Metasurfaces are an emerging technology that may supplant many of the conventional optics found in imaging devices, displays, and precision scientific instruments. Here, we develop a method for designing optical systems composed of multiple unique metasurfaces aligned in sequence. Our approach is based on computational inverse design, also known as the adjoint-gradient method. This technique enables thousands or millions of independent design variables to be optimized in parallel, with little or no intervention required by the user. To demonstrate the broad applicability of our method, we use it to design an achromatic doublet metasurface lens, a spectrally-multiplexed holographic element, and an ultra-compact optical neural network for classifying handwritten digits.

21 pages, 5 figures

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• Differentiable Scattering Matrix for Optimization of Photonic Structures
• Broadband Polarization-Independent Achromatic Metalenses with Unintuitively-Designed Random-Shaped Meta-Atoms
• On-chip rewritable phase-change metasurface for programmable diffractive deep neural networks
• Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
• Gradient-based optimization of scatterer arrangements based on the T-matrix method