2022
DOI: 10.29026/oes.2022.210012
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Benchmarking deep learning-based models on nanophotonic inverse design problems

Abstract: Photonic inverse design concerns the problem of finding photonic structures with target optical properties. However, traditional methods based on optimization algorithms are time-consuming and computationally expensive. Recently, deep learning-based approaches have been developed to tackle the problem of inverse design efficiently. Although most of these neural network models have demonstrated high accuracy in different inverse design problems, no previous study has examined the potential effects under given c… Show more

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Cited by 61 publications
(20 citation statements)
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“…Moreover, as topology optimization is a gradient-based optimization method, the presented freeform metalens are locally optimal rather than globally optimal, which means that the focusing efficiency could be further improved. In fact, deep neural network could serve as a kind of robust global optimizers design by combining topology optimization, [49,50] offering a promising direction for large-scale meta-devices design.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, as topology optimization is a gradient-based optimization method, the presented freeform metalens are locally optimal rather than globally optimal, which means that the focusing efficiency could be further improved. In fact, deep neural network could serve as a kind of robust global optimizers design by combining topology optimization, [49,50] offering a promising direction for large-scale meta-devices design.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, recent years have seen significant advances in guided-mode conversion applications. One direct method is matching the refractive index difference between different modes by the phase gradient induced equivalent wavevector. , Nevertheless, for complex and multifunctional devices, an inverse design strategy is preferred due to the high freedom-of-degree of subwavelength structures. …”
Section: Chip-integrated Metasurface and Integrated Imagermentioning
confidence: 99%
“…For example, by matching the geometric phases of nano-slots on silver to specific superimpositions of the inward and outward surface plasmon profiles for the two spins, arbitrary spin-dependent orbitals can be generated in a slot-free region [58] . In addition to the traditional design approach, heuristic design methodology has been adopted in nanophotonics to improve the design efficiency and device performances [59] .…”
Section: Geometric Phases In Spatially Rotated Anisotropic Elementsmentioning
confidence: 99%