Machine learning aided inverse design for flattop beam fiber

Guo, Yinghao; Cheng, Yudan; Jiang, Youchao; Cao, Min; Tang, Min; Ren, Wei

doi:10.1016/j.optcom.2022.128814

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…微纳光学的设计对象有面向通信领域的光栅耦合器 [51] 、微结构光纤 [48] 、模式耦合器 [52] 、多模波导 [53] 相位噪声滤波器 [54] ; 面向材料领域的等离子体材料 [15] 、超表面的研究 [55] ; 面向传感领域的等离子体-声子耦合器 [56] 、X射线瞬态光栅 [57] 及光伏领域的太阳能电池 [58]…”

Section: 微纳光学设计对象unclassified

“Machine micro/nano optics scientist”: Application and development of artificial intelligence in micro/nano optical design

Chen-Yang¹,

Meng²,

Yi-Ming³

et al. 2023

Acta Phys. Sin.

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Micro/nano optical materials and devices are the key to many optical fields such as optical communication, optical sensing, biophotonics, laser, quantum optics, etc. At present, the design of micro/nano optics mainly relies on the simulation of numerical methods such as Finite-difference time-domain (FDTD), Finite element method (FEM) and Finite difference method (FDM), which These methods are bottlenecks in current micro/nano optical design because of their dependence on computational resources, low innovation efficiency, and difficulties in obtaining global optimal design. Artificial intelligence (AI) has brought a new paradigm of scientific research: AI for Science, which has been successfully applied to chemistry, materials science, quantum mechanics, and particle physics. In the area of micro/nano design AI has been applied to the design research of chiral materials, power dividers, microstructured optical fibers, photonic crystal fibers, chalcogenide solar cells, plasma waveguides and so on. According to the characteristics of the micro/nano optical design objects, the data sets can be constructed in the form of parameter vectors for complex micro/nano optical designs such as hollow core anti-resonant fibers with multi-layer nested tubes, and in the form of images for simple micro/nano optical designs such as 3dB couplers. The constructed datasets are trained with artificial neural network, deep neural network and convolutional neural net algorithms to build regression or classification tasks for performance prediction or inverse design of micro/nano optics. The constructed AI models are optimized by adjusting performance evaluation metrics such as mean square error, mean absolute error, and binary cross entropy. In this paper, the application of AI in micro/nano optics design is reviewed, the application methods of AI in micro/nano optics are summarized, and the difficulties and future development trends of AI in micro/nano optics research are analyzed and prospected.

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Section: 微纳光学设计对象unclassified

“Machine micro/nano optics scientist”: Application and development of artificial intelligence in micro/nano optical design

Chen-Yang¹,

Meng²,

Yi-Ming³

et al. 2023

Acta Phys. Sin.

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Artificial intelligence designer for optical Fibers: Inverse design of a Hollow-Core Anti-Resonant fiber based on a tandem neural network

et al. 2023

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Optimization of a quantum cascade laser cavity for single-spatial-mode operation via machine learning

Jacobs,

Kirch,

et al. 2023

APL Machine Learning

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Neural networks, trained with the ADAM algorithm followed by a globally convergent modification to Newton’s method, are developed to predict the threshold gain of the fundamental and first higher-order modes as functions of the refractive-index profile in a quantum cascade laser cavity. The networks are used to optimize the design of a refractive-index profile that provides essentially single-spatial-mode performance in a nominally multi-moded cavity by maximizing the threshold-gain differential between the modes. The use of neural networks allows the optimization to be performed in seconds, instead of days or weeks which would be required if Maxwell’s equations were repeatedly solved to obtain the threshold gains.

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Machine learning aided inverse design for flattop beam fiber

Cited by 3 publications

References 24 publications

“Machine micro/nano optics scientist”: Application and development of artificial intelligence in micro/nano optical design

“Machine micro/nano optics scientist”: Application and development of artificial intelligence in micro/nano optical design

Artificial intelligence designer for optical Fibers: Inverse design of a Hollow-Core Anti-Resonant fiber based on a tandem neural network

Optimization of a quantum cascade laser cavity for single-spatial-mode operation via machine learning

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