An approach to design photonic crystal gas sensor using machine learning

Ghosh, Alekhya; Pal, Arghadeep; Das, N. R.

doi:10.1016/j.ijleo.2019.163997

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…The application of machine learning algorithms in the design of sensitive sensors is a critical aspect of data analysis, pattern recognition, and sensitivity enhancement [13][14][15][16][17]. Machine learning algorithms help in the processing of raw data collected from sensors into cleaner and more accurate data sets using various data processing techniques such as data preprocessing, estimation of missing data, data multiplexing, and noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Design of classification-based photonic crystal sensor for chemical substance detection

Zeydan Çelen,

Karlik

2024

Machine Learning in Photonics

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Photonic crystals are periodic structures with refractive index changes in one, two, or three dimensions. Due to their unique design, these crystals exhibit a photonic band gap that allows light to propagate through the structure at specific frequencies and be reflected at other frequencies. In regions where light cannot pass, known as the forbidden band gap, certain photonic states are created by deliberately creating defects in the crystal. These are called defect modes. By analyzing the dispersion curve of the defect mode, valuable information can be obtained about the behavior of light within the structure. This information includes the group velocity of the light, group velocity dispersion, and sensor sensitivity.This study proposes a two-dimensional square lattice symmetry photonic crystal design. This design arranges dielectric rods on a low refractive index material according to the square lattice symmetry. The dispersion curve of the defect mode obtained through the created line defect in the structure is investigated, and the change in group velocity of the propagating light within the structure is obtained. Increasing the sensor sensitivity is achieved by reducing the group velocity of the propagating light. Classification-based machine learning methods are employed to detect chemical substances, and the performance rates of these methods are compared for chemical substance detection.

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Section: Introductionmentioning

confidence: 99%

Design of classification-based photonic crystal sensor for chemical substance detection

Zeydan Çelen,

Karlik

2024

Machine Learning in Photonics

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“…Although increasing the number of defects in a DPC modifies the absorption value, wavelength, and Full Width at Half Maximum (FWHM) of defect modes, it complicates the design because of structural parameters’ abundance that leads researchers to use machine learning techniques. In the field of PCs, machine learning is being used to design and optimize a wide range of devices and structures, such as optical waveguides, resonant cavities, and optical sensors 41 – 43 . To design DPC structures and predict their properties, various machine learning methods such as linear, polynomial, and (KNN) regression are implemented through training a model on a training subset and evaluating its validity on a test subset to improve its generalization ability 44 – 47 .…”

Section: Introductionmentioning

confidence: 99%

Data analysis on the three defect wavelengths of a MoS2-based defective photonic crystal using machine learning

Ansari,

Sohrabi,

Mirbaghestan

et al. 2023

Sci Rep

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To reduce the dimension of optoelectronic devices, recently, Molybdenum disulfide (MoS2) monolayers with direct bandgap in the visible range are widely used in designing a variety of photonic devices. In these applications, adjustability of the working wavelength and bandwidth with optimum absorption value plays an important role. This work proposes a symmetric defective photonic crystal with three defects containing MoS2 monolayer to achieve triple narrowband defect modes with wavelength adjustability throughout the Photonic Band Gap (PBG) region, 560 to 680 nm. Within one of our designs remarkable FWHM approximately equal to 5 nm with absorption values higher than 90% for the first and third defect modes are achieved. The impacts of varying structural parameters on absorption value and wavelength of defect modes are investigated. Due to the multiplicity of structural parameters which results in data plurality, the optical properties of the structure are also predicted by machine learning techniques to assort the achieved data. Multiple Linear Regression (MLR) modeling is used to predict the absorption and wavelength of defect modes for four datasets based on various permutations of structural variables. The machine learning modeling results are highly accurate due to the obtained R2-score and cross-validation score values higher than 90%.

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An On-Demand Inverse Design Method for Nanophotonic Devices Based on Generative Model and Hybrid Optimization Algorithm

Zhu,

Li,

Yang

et al. 2023

Plasmonics

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An approach to design photonic crystal gas sensor using machine learning

Cited by 10 publications

References 13 publications

Design of classification-based photonic crystal sensor for chemical substance detection

Design of classification-based photonic crystal sensor for chemical substance detection

Data analysis on the three defect wavelengths of a MoS2-based defective photonic crystal using machine learning

An On-Demand Inverse Design Method for Nanophotonic Devices Based on Generative Model and Hybrid Optimization Algorithm

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