Comparison of Methods for Fiber Bragg Gratings Simulation

Helan, Radek

doi:10.1109/isse.2006.365378

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Figure 2 (d) compares the computation speed for the ANN to the eigenmode solver, Meep Photonic Bands (MPB). This significant speedup enables many simulation techniques, like the layered dielectric media transfer matrix method (LDMTMM) [14] or the eigenmode expansion method (EMM) [15], where photonic components are discretized into individual waveguides. Using the ANN, a transfer matrix for each waveguide can be quickly generated and cascaded to formulate a fairly accurate response for the device.…”

Section: Waveguide Neural Networkmentioning

confidence: 99%

“…On the same HPC, we generated our Bragg training set by simulating 104,131 different gratings with the layered dielectric media transfer matrix method (LDMTMM) [14]. The LDMTMM method models each individual section of the Bragg grating as an ideal waveguide and cascades each sections's corresponding transfer matrix to estimate the grating's response for each wavelength point of interest.…”

Section: Training Data Generation and Preprocessingmentioning

confidence: 99%

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Designing integrated photonic devices using artificial neural networks

Hammond

Camacho

2019

Opt. Express

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We develop and experimentally validate a novel artificial neural network (ANN) design framework for silicon photonics devices that is both practical and intuitive. As case studies, we train ANNs to model both strip waveguides and chirped Bragg gratings using a small number of simple input and output parameters relevant to designers. Once trained, the ANNs decrease the computational cost relative to traditional design methodologies by more than 4 orders of magnitude. To illustrate the power of our new design paradigm, we develop and demonstrate both forward and inverse design tools enabled by the ANN. We use these tools to design and fabricate several integrated Bragg grating devices. The ANN's predictions match very well with the experimental measurements and do not require any post-fabrication training adjustments.

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Section: Waveguide Neural Networkmentioning

confidence: 99%

Section: Training Data Generation and Preprocessingmentioning

confidence: 99%

Designing integrated photonic devices using artificial neural networks

Hammond

Camacho

2019

Opt. Express

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“…To accurately simulate the the ICBG reflection and group delay responses for our training-set, we used a layered-dielectric media transfer matrix method (LDMTMM) [15]. The method discretizes the ICBG into individual dielectric slabs, models each slab as an ideal waveguide, and propogates the fields through each slab using a transfer matrix.…”

Section: Data Generationmentioning

confidence: 99%

Accelerating silicon photonic parameter extraction using artificial neural networks

2019

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We present a novel silicon photonic parameter extraction tool that uses artificial neural networks. While other parameter extraction methods are restricted to relatively simple devices whose responses are easily modeled by analytic transfer functions, this method is capable of extracting parameters for any device with a discrete number of design parameters. To validate the method, we design and fabricate integrated chirped Bragg gratings. We then estimate the actual device parameters by iteratively fitting the simultaneously measured group delay and reflection profiles to the artificial neural network output. The method is fast, accurate, and capable of modeling the complicated chirping and index contrast.

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“…A first possible approach consists in the numerical resolution of Equations (2) and (3) , typically by means of a Runge-Kutta scheme. However, even if an appropriate numerical scheme is applied, this method is slow if compared with other possible approaches having the same accuracy [ 17 , 18 ]. An alternative, widely used and relatively fast method is based on the piecewise-uniform approximation of the coefficients σ̂ , k and k *.…”

Section: Developed Strain Reconstruction Technique: the Fbgmentioning

confidence: 99%

Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors

Bettini

Guerreschi

Sala

2015

Sensors

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The interest of the aerospace industries in structural health and usage monitoring systems is continuously increasing. Among the techniques available in literature those based on Fibre Bragg Grating sensors are much promising thanks to their peculiarities. Different Chirped Bragg Grating sensor configurations have been investigated in this paper. Starting from a numerical model capable of simulating the spectral response of a grating subjected to a generic strain profile (direct problem), a new code has been developed, allowing strain reconstruction from the experimental validation of the program, carried out through different loading cases applied on a chirped grating. The wavelength of the reflection spectrum for a chirped FBG has a one-to-one correspondence to the position along the gauge section, thus allowing strain reconstruction over the entire sensor length. Tests conducted on chirped FBGs also evidenced their potential for SHM applications, if coupled with appropriate numerical strain reconstructions tools. Finally, a new class of sensors—Draw Tower Grating arrays—has been studied. These sensors are applicable to distributed sensing and load reconstruction over large structures, thanks to their greater length. Three configurations have been evaluated, having different spatial and spectral characteristics, in order to explore possible applications of such sensors to SHM systems.

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Comparison of Methods for Fiber Bragg Gratings Simulation

Cited by 3 publications

References 13 publications

Designing integrated photonic devices using artificial neural networks

Designing integrated photonic devices using artificial neural networks

Accelerating silicon photonic parameter extraction using artificial neural networks

Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors

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