Design of broadband omnidirectional antireflection coatings using ant colony algorithm

Guo, Xia; Zhou, Huiyang; Guo, Shijun; Luan, Xinxin; Cui, Wenda; F., Y.; Shi, Liang

doi:10.1364/oe.22.0a1137

Cited by 25 publications

(19 citation statements)

References 19 publications

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“…Designing MLTFs that feature a particular target reflectivity or transitivity over angle of incidence and wavelength is a common engineering challenge and therefore many optimization methods have been developed: Some of them are based on gradients 26 or biological inspirations 27,28 . Recently, even some approaches including neural networks [29][30][31] or reinforcement learning 32,33 have been implemented to efficiently scan the search space for suitable MLTF designs.…”

Section: State Of the Artmentioning

confidence: 99%

“…To measure 2 how close a particular MLTF's optical characteristic is to an optimal one takes not even a second in total. However, in contrast with applications like anti-reflection coatings 28 , a specific optimal optical characteristic that causes an MLTF to increase the directionality of white light is not known a priori. To circumvent this lack of information, we conduct noisy ray tracing simulations in order to optimize the power and color point in forward direction regarding the layer thicknesses of an MLTF.…”

Section: State Of the Artmentioning

confidence: 99%

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Playing Ping Pong With Light: Directional Emission of White Light

Wankerl

Wiesmann

Kreiner

et al. 2021

Preprint

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Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Playing Ping Pong With Light: Directional Emission of White Light

Wankerl

Wiesmann

Kreiner

et al. 2021

Preprint

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show abstract

“…Although the existence of a global optimum is mathematically and computationally evinced [14,59,60], algorithms that guarantee finding the global optimum in an exhaustive search tend to be computationally intractable, even if only layer thicknesses of less than four layers in total are considered [16]. Thus, in accordance with some theoretical and analytical investigations [1,15,28,68,71], including genetic and evolutionary approaches, multi-layer thin films are also optimized based on heuristic approaches [10,21,29,41,44,67]. Alike many of the mentioned methods, deep learning-assisted techniques are reported to optimize layer thicknesses only: Roberts et al [47] proposed a variational autoencoder, Liu et al [36] combined forward modeling and inverse design in a tandem of DNNs, and Hegde [23] blended deep learning with evolutionary elements.…”

Section: Introductionmentioning

confidence: 99%

Parameterized reinforcement learning for optical system optimization

Wankerl

Stern

Mahdavi

et al. 2021

J. Phys. D: Appl. Phys.

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Engineering a physical system to feature designated characteristics states an inverse design problem, which is often determined by several discrete and continuous parameters. If such a system must feature a particular behavior, the mentioned combination of both, discrete and continuous, parameters results in a challenging optimization problem that requires an extensive search for an optimal system design. However, if the corresponding inverse design problem can be reformulated as a parameterized Markov decision process, reinforcement learning (RL) provides a heuristic framework to solve it. In this work, we use multi-layer thin films as an example of the aforementioned optimization problems and consider three design parameters: Each of the thin film layer's dielectric material (discrete) and thickness (continuous), as well as the total number of layers (discrete). While recent methods merely determine the optimal thicknesses and-less commonly-the layers' materials, our approach optimizes the total number of stacked layers as well. In summary, we further develop a Q-learning variant to solve inverse design optimization and thereby outperform human experts and current approaches like needle-point optimization or naive RL. For this purpose, we propose an exponentially transformed reward signal that eases policy search and enables constrained optimization. Moreover, the learned Q-values contain information about the optical properties of multi-layer thin films, which allows us a physical interpretation or what-if analysis and thus enables explainability.

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“…Chang and Lee applied the generalized simulated-annealing method (GSAM) for the thin-film system design and discovered that there would be no problem with the trapping local minimum that happened in the design 3 . Recently, the researchers worked in this field also have applied to the optical coating optimization methods with several models such as particle swarm optimization (PSO) 4 , genetic algorithm (GA) 5 , 6 , ant colony algorithm 7 and deep learning algorithm 8 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Multilayer optical thin film design with deep Q learning

Jiang

Yoshie

Chen

2020

Sci Rep

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Multilayer optical film plays a significant role in broad fields of optical application. Due to the nonlinear relationship between the dispersion characteristics of optical materials and the actual performance parameters of optical thin films, it is challenging to optimize optical thin film structure with the traditional models. In this paper, we present an implementation of Deep Q-learning, which suited for the most part for optical thin film. As a set of concrete demonstrations, we optimize solar absorber. The optimal program could optimal this solar absorber in 500 epoch (about 200 steps per-epoch) without any human intervention. Search results perform better than researchers’ manual searches.

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Design of broadband omnidirectional antireflection coatings using ant colony algorithm

Cited by 25 publications

References 19 publications

Playing Ping Pong With Light: Directional Emission of White Light

Playing Ping Pong With Light: Directional Emission of White Light

Parameterized reinforcement learning for optical system optimization

Multilayer optical thin film design with deep Q learning

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