Inverse Design of Metasurfaces Based on Coupled-Mode Theory and Adjoint Optimization

Lightweight, miniaturized optical imaging systems are vastly anticipated in these fields of aerospace exploration, industrial vision, consumer electronics, and medical imaging. However, conventional optical techniques are intricate to downscale as refractive lenses mostly rely on phase accumulation. Metalens, composed of subwavelength nanostructures that locally control light waves, offers a disruptive path for small-scale imaging systems. Recent advances in the design and nanofabrication of dielectric metalenses have led to some high-performance practical optical systems. This review outlines the exciting developments in the aforementioned area whilst highlighting the challenges of using dielectric metalenses to replace conventional optics in miniature optical systems. After a brief introduction to the fundamental physics of dielectric metalenses, the progress and challenges in terms of the typical performances are introduced. The supplementary discussion on the common challenges hindering further development is also presented, including the limitations of the conventional design methods, difficulties in scaling up, and device integration. Furthermore, the potential approaches to address the existing challenges are also deliberated.

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Section: Common Challenges For Further Developmentmentioning

confidence: 99%

“…An example is shown in Fig. 18c 191 . A physical model, the couple-mode-theory (CMT) model, is first built by previous simulations.…”

Section: Common Challenges For Further Developmentmentioning

confidence: 99%

Dielectric metalens for miniaturized imaging systems: progress and challenges

et al. 2022

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“…However, LPA is inaccurate whenever the cell-to-cell variation is large [71][72][73][74][75] and cannot describe nonlocal responses [76,77] and metasurfaces that are not based on unit cells [78][79][80]. Coupled-mode theory can model the coupling between meta-atoms [81] but requires isolated resonances with high quality factors. Full-wave simulation remains the gold standard but currently requires enormous computing resources.…”

Section: Large-area Metasurfacesmentioning

confidence: 99%

Fast multi-source nanophotonic simulations using augmented partial factorization

Lin,

Wang,

Hsu

2022

Preprint

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Multi-channel optical systems such as disordered media, metasurfaces, multi-mode fibers, and photonic circuits are of both fundamental and technological importance. However, their optical responses are difficult to describe in the presence of multiple scattering, strong coupling, and multipath interference. Accurate modeling requires many large-scale simulations associated with the many input states, which currently take enormous computing resources. We propose an approach where all simulations are solved jointly and efficiently by augmenting the Maxwell operator with the source and the projection profiles, followed by a single partial factorization. This method applies to any linear system with any type of inputs, and benchmarks show it uses less memory and is 1,000 to 30,000,000 times faster than existing methods. It opens the door to previously inaccessible studies across disciplines involving multi-channel wave transport. As pilot examples, we realize, for the first time, full-wave simulations of entangled-photon backscattering from disorder and all-angle characterizations of aperiodic metasurfaces that are thousands of wavelengths wide.

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“…Maxwell simulators are essential tools for the characterization and design of electromagnetic systems. These systems operate at frequencies spanning the radiowave to X-ray and include a diversity of antennas, − diffractive surfaces, − metamaterials, − and guided wave-based photonic circuits. − Among the most popular frequency domain Maxwell solvers are the finite element method (FEM) , and finite difference frequency domain (FDFD) algorithms. − In both algorithms, the system domain is subdivided into discrete voxels, and the simulator solves for electromagnetic fields by constructing and inverting a sparse matrix with dimensions proportional to the total number of voxels. While these methods can be used to accurately solve general electromagnetics problems, the time and computation cost of matrix inversion serve as practical bottlenecks for the simulation of large domains and for design tasks, where large numbers of electromagnetic simulations are required to perform iterative optimization.…”

Section: Introductionmentioning

confidence: 99%

High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning

et al. 2022

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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.

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Inverse Design of Metasurfaces Based on Coupled-Mode Theory and Adjoint Optimization

Cited by 64 publications

References 42 publications

Dielectric metalens for miniaturized imaging systems: progress and challenges

Dielectric metalens for miniaturized imaging systems: progress and challenges

Fast multi-source nanophotonic simulations using augmented partial factorization

High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning

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