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Photonic Crystal Nanobeam Cavity With a High Experimental Q Factor Exceeding Two Million Based on Machine Learning

We propose and demonstrate a six-hole tapered silicon photonic crystal nanobeam cavity with a theoretical high quality ( Q ) factor and an ultrasmall mode volume based on machine learning. The crucial element to efficiently obtain high Q factors is to take the prediction result of the designed neural network as the fitness function of the genetic algorithm, whose evolution direction is developing towards higher fitness. Consequently, by combining the neural network and genetic algorithm iteration, an optimized photonic crystal nanobeam cavity with a theoretical Q factor ( Qth ) as high as 1.2 × 10 8 and an ultrasmall mode volume of 0.32( λ / n ) 3 is obtained. Leveraging the resonant scattering optical method, the cavity experimental Q factor ( Qexp ) is measured as 2.17 × 10 6 , which is a record high experimental Q factor of silicon photonic crystal nanobeam cavity with maintaining an ultrasmall mode volume of 0.32( λ / n ) 3 and an ultra-compact device size of 6 μm 2 . Owing to the ultra-high Q factor-to-mode volume ratio, the proposed photonic crystal nanobeam cavities could extremely enhance the interactions between light and matter, which have extensive important applications in low-threshold optical lasers, high-resolution filters and sensors.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.21 November 1, 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7150-7158

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
GENETIC ALGORITHM
HIGH Q FACTOR
PHOTONIC CRYSTAL NANOBEAM CAVITY
ULTRASMALL MODE VOLUME

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3199764

Pernyataan Tanggungjawab

Li Liu, Chenggong Ma, Mengyuan Ye, Zhihua Yu, Wei Xue, Zhihao Hu, Jian Li

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