Deep learning for the design and characterization of high efficiency self-focusing grating

Pu, Tanchao; Cao, Fulin; Liu, Ziwei; Xie, Changqing

doi:10.1016/j.optcom.2022.127951

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…In addition, modern industrial design is based on visual culture, through the organic integration of art and technology, in order to increase industrial production and enhance the popularity of industrial products. Therefore, the vigorous development of industrial design is an inevitable trend of the times, which has important practical significance for the inheritance of human civilization and the dissemination of art and culture [1,2].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Zhang

2024

SCPE

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In order to understand the application of degree learning algorithms in industrial design visual optimization, the author proposes an application research based on deep learning algorithms in industrial design visual optimization. The author first starts with the basic principles of deep learning algorithms and provides a detailed explanation of the basic structure of the single-layer network of deep learning algorithms, as well as the restricted Boltzmann machine and its training process. Finally, an example of the performance improvement brought by the application of deep learning technology in handwritten digit recognition was given through an automatic encoder, and a simple summary of deep learning technology was made. Secondly, the NCI matching algorithm is used to match industrial design products and reconstruct the point cloud of industrial products to accurately detect their features. On this basis, deep learning algorithms are applied to construct a visual optimization model for industrial design, determine the output format of the model scene and the output situation of industrial design, and process the model according to the changing characteristics of the industrial design model to comprehensively edit the data of the model. Finally, targeted optimization of the industrial design visual optimization model is carried out based on the technical characteristics of deep learning algorithms, in order to complete the industrial design visual optimization. The experimental comparison results show that the industrial design visual optimization method optimized in this design has higher visual clarity than the traditional industrial design method, reaching 98%. Greatly improves the visual clarity of industrial design.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Zhang

2024

SCPE

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Hexagonal diffraction gratings generated by convolutional neural network-based deep learning for suppressing high-order diffractions

Hu,

Ding,

et al. 2024

J. Opt. Soc. Am. A

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The st order diffraction of gratings is widely used in spectral analysis. However, when the incident light is non-monochromatic, the higher-order diffractions generated by traditional diffraction gratings are always superimposed on the useful first-order diffraction, complicating subsequent spectral decoding. In this paper, single-order diffraction gratings with a sinusoidal transmittance, called hexagonal diffraction gratings (HDGs), are designed using a convolutional neural network based on deep learning algorithm. The trained convolutional neural network can accurately retrieve the structural parameters of the HDGs. Simulation and experimental results confirm that the HDGs can effectively suppress higher-order diffractions above the third order. The intensity of third-order diffraction is reduced from 20% of the first-order diffraction to less than that of the background. This higher-order diffraction suppression property of the HDGs is promising for applications in fields such as synchrotron radiation, astrophysics, and soft x-ray lasers.

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Inverse design of an on-chip optical response predictor enabled by a deep neural network

Kim¹,

Neşeli²,

Kim³

et al. 2023

Opt. Express

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We proposed inverse-designed nanophotonic waveguide devices which have the desired optical responses in the wide band of 1450-1650 nm. The proposed devices have an ultra-compact size of just 1.5 µm × 3.0 µm and are designed on a silicon-on-insulator (SOI) waveguide platform. Individual nano-pixels with dimensions of 150 nm × 150 nm were made of either silicon or silicon dioxide, and the materials for the 200 total cells were determined using a trained deep neural network. While training the two networks, the hyperparameter optimization method was applied to make the training process efficient. We then fabricated the proposed devices using a CMOS-compatible fabrication process, and experimentally verified the fabricated device performance.

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Deep learning for the design and characterization of high efficiency self-focusing grating

Cited by 5 publications

References 27 publications

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Application of Deep Learning Algorithm in Visual Optimization of Industrial Design

Hexagonal diffraction gratings generated by convolutional neural network-based deep learning for suppressing high-order diffractions

Inverse design of an on-chip optical response predictor enabled by a deep neural network

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