Controlling the minimal feature sizes in adjoint optimization of nanophotonic devices using b-spline surfaces

Khoram, Erfan; Qian, Xiaoping; Yuan, Ming; Yu, Zongfu

doi:10.1364/oe.384438

Cited by 26 publications

(15 citation statements)

References 29 publications

(33 reference statements)

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“…15,16 The ability for topology optimization to explore exceptionally large design spaces without approximations to Maxwell's equations has led to demonstrations of freeform metagratings, 6,17,18 metalenses, 7,11,12 and on-chip photonic devices 19 with exceptional performance. However, hard minimum feature size tolerances 20−23 and robustness criteria 24−26 can be difficult to incorporate into topology optimized designs, even with heuristic methods that have been developed to address these issues, [27][28][29]42 and there remain experimental challenges to accurately manufacturing complex freeform shapes. In addition, the operating principles of these devices lack straightforward physical interpretability.…”

Section: ■ Introductionmentioning

confidence: 99%

“…For photonics, the most versatile and computationally efficient inverse design algorithms are based on freeform topology optimization, − in which every dielectric voxel in the simulation domain is a free parameter that can be modified using the adjoint variables method , or autodifferentiation. , The ability for topology optimization to explore exceptionally large design spaces without approximations to Maxwell’s equations has led to demonstrations of freeform metagratings, ,, metalenses, ,, and on-chip photonic devices with exceptional performance. However, hard minimum feature size tolerances − and robustness criteria − can be difficult to incorporate into topology optimized designs, even with heuristic methods that have been developed to address these issues, − , and there remain experimental challenges to accurately manufacturing complex freeform shapes. In addition, the operating principles of these devices lack straightforward physical interpretability.…”

Section: Introductionmentioning

confidence: 99%

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Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices

et al. 2022

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We propose a three-dimensional freeform nanophotonic platform in which wavelength-scale domains comprise basic geometric structures with explicitly defined dimensions, positions, orientations, and minimum feature size constraints. Given a desired wavefront shaping objective, these parameters can be collectively optimized using gradient-based shape optimization with full accounting of near-field interactions between structures. We apply our concept to a variety of metagratings supporting high diffraction efficiencies and polarization control, and we experimentally demonstrate a device with a tailored polarization response as a function of wavelength. The combination of device capability, feature size constraints, and ease of manufacturability enabled by our methodology will facilitate the development of robust, high performance, nanophotonic technologies.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices

et al. 2022

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“…Current methods to impose MFS constraints fall in one of three classes. The first is to set the device voxel dimensions or spacing between features to match the desired MFS [27][28][29]. While this method works, it adds significant granularity to the device design space, limiting final device performance.…”

Section: Introductionmentioning

confidence: 99%

Design space reparameterization enforces hard geometric constraints in inverse-designed nanophotonic devices

Chen¹,

Jiang²,

Fan³

2020

Preprint

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Inverse design algorithms are the basis for realizing high-performance, freeform nanophotonic devices. Current methods to enforce geometric constraints, such as practical fabrication constraints, are heuristic and not robust. In this work, we show that hard geometric constraints can be imposed on inverse-designed devices by reparameterizing the design space itself. Instead of evaluating and modifying devices in the physical device space, candidate device layouts are defined in a constraint-free latent space and mathematically transformed to the physical device space, which robustly imposes geometric constraints. Modifications to the physical devices, specified by inverse design algorithms, are made to their latent space representations using backpropagation. As a proof-of-concept demonstration, we apply reparameterization to enforce strict minimum feature size constraints in local and global topology optimizers for metagratings. We anticipate that concepts in reparameterization will provide a general and meaningful platform to incorporate physics and physical constraints in any gradient-based optimizer, including machine learning-enabled global optimizers.

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“…Different approaches are proposed to solve this issue, such as density filters [75], penalty functions [76], artificial damping [50] and morphological filters [77]. Recently, Khoram et al introduced b-splines to control minimal feature size of irregular structures [78]. Instead of filtering out small features directly, this method transfers the design domain to lower space dimension composed of b-spline functions, which damps small features internally.…”

mentioning

confidence: 99%

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

et al. 2021

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Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

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Controlling the minimal feature sizes in adjoint optimization of nanophotonic devices using b-spline surfaces

Cited by 26 publications

References 29 publications

Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices

Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices

Design space reparameterization enforces hard geometric constraints in inverse-designed nanophotonic devices

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

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