Optimization and uncertainty quantification of gradient index metasurfaces [Invited]

Abstract

The design of intrinsically flat two-dimensional optical components, i.e., metasurfaces, generally requires an extensive parameter search to target the appropriate scattering properties of their constituting building blocks. Such design methodologies neglect important near-field interaction effects, playing an essential role in limiting the device performance. Optimization of transmission, phase-addressing and broadband performances of metasurfaces require new numerical tools. Additionally, uncertainties and systematic fabrication errors should be analysed. These estimations, of critical importance in the case of large production of metaoptics components, are useful to further project their deployment in industrial applications. Here, we report on a computational methodology to optimize metasurface designs. We complement this computational methodology by quantifying the impact of fabrication uncertainties on the experimentally characterized components. This analysis provides general perspectives on the overall metaoptics performances, giving an idea of the expected average behavior of a large number of devices.

Mid-Infrared Grayscale Metasurface Holograms

“…From the raw data of the measured image, we estimate that the unwanted zeroth-order perturbation is 19%, indicating that the total efficiency of our metasurface-based hologram is considerably high, up to 81%. We would like to underline the fact that the reported performance could be further increased, notably by properly considering near-field coupling of metasurface building blocks using optimization design methods [30][31][32][33][34][35].…”

Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data

“…Readers interested in the latter case are referred to Ref. 32 The fundamental scattering parameter is considered as QoI Q y ð Þ ℂ. We consider three sensitive geometrical parameters as uncertain, in particular the thicknesses of the upper gold layer t 1 = 12 nm + Δy 1 , the thickness of the dielectric layer t 2 = 14 nm + Δy 2 and the grating depth T = 20 nm + Δy 3 , as illustrated in Figure 6.…”

Feature issue introduction: advanced computational nanophotonics: from materials to devices

“…We open by noting that there has been an exceptional amount of interest in robust methodologies for highly-efficient design optimization because of their ultimate importance in shortening the design cycle as well as their ability to offer innovative solutions not attainable otherwise. Hence, it comes as no surprise that our issue presents three papers dealing with various aspects of optimization techniques [1][2][3]. In [1], Campbell et al, give a brief review of the most popular techniques, and then provide a detailed discussion of the most recent innovative methods such as topology optimization (TO) and multi-objective optimization.…”

“…Analyzing the robust devices, the authors show that robustness is enforced through rather non-intuitive interactions between excited robust modes and may not be attained with simple design rules [2]. Genevet et al discuss a computational approach to enable robust metasurface designs by quantifying the impact of fabrication errors on the experimentally fabricated devices [3]. The approach can be useful in designing novel passive and active metasurface components where fabrication deviations often pose a challenge and need to be properly accounted for in the design phase.…”

“…The special focus of this Feature Issue is also put on new approaches built on a multiphysics computational environment that enables tighter integration between different phenomena involved in light-matter interactions and offers a broader range of new modeling opportunities. The Feature Issue presents eleven papers [1][2][3][4][5][6][7][8][9][10][11] that fit a joint theme of Advanced Computational Nanophotonics: From Materials to Devices. The science and art of computational photonics have a long history, yet in the last decade interest in new approaches to advanced multiphysics modeling at the nanoscale and innovative multi-objective optimization techniques for nanophotonics have been growing exponentially and are now the subject of intense cross-disciplinary research efforts.…”