Numerical Dispersion Compensation for FDTD via Deep Learning

Qi, Shutong; Sarris, Costas D.

doi:10.1109/ap-s/usnc-ursi47032.2022.9886317

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Numerous deep learning based approaches have been demonstrated to accelerate first-order electromagnetic simulations, ranging from an implementation of the perfectly matched layer boundary condition to reduce ill-conditioning of the coefficient matrices [11,12] over reduction of numeric dispersion [13] to employing surrogate models for the optimization of periodic nanostructures [14,15]. While the acceleration of simulation algorithms using deep learning in the context of metamaterials has gained great popularity, the application to pixel-discrete inverse designed structures featuring complex and non-periodic geometries poses new challenges.…”

Section: Introductionmentioning

confidence: 99%

Accelerating finite-difference frequency-domain simulations of inverse designed structures in nanophotonics using deep learning

Schulte,

Butz,

Becker

et al. 2024

J. Opt. Soc. Am. B

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The inverse design of nanophotonic devices is becoming increasingly relevant for the development of complex photonic integrated circuits. Electromagnetic first-order simulations contribute to the overwhelming computational cost of the optimization routines in established inverse design algorithms, requiring more efficient methods for enabling improved and more complex design process flows. Here we present such a method to predict the electromagnetic field distribution for pixel-discrete planar inverse designed structures using deep learning. Our model is able to infer accurate predictions used to initialize a conventional finite-difference frequency-domain algorithm and thus lowers the average time required for simulating the electromagnetic response of nanophotonic device layouts by up to 53% in iterative design process flows. We demonstrate the applicability of our deep learning method for the inverse design of photonic integrated powersplitters and mode converters, and we highlight the possibility of exploiting previous learning results in subsequent design tasks of novel functionalities via fine-tuning reduced data sets, thus improving computational speed further.

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Section: Introductionmentioning

confidence: 99%

Accelerating finite-difference frequency-domain simulations of inverse designed structures in nanophotonics using deep learning

Schulte,

Butz,

Becker

et al. 2024

J. Opt. Soc. Am. B

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Super-Resolution Deep Learning Network for Finite-Difference Time-Domain Simulations

Li,

Liu,

Tan

2024

Antennas Wirel. Propag. Lett.

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Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

Sarris

2022

IEEE Trans. Microwave Theory Techn.

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This paper demonstrates a deep learning based methodology for the rapid simulation of planar microwave circuits based on their layouts. We train convolutional neural networks to compute the scattering parameters of general, twoport circuits consisting of a metallization layer printed on a grounded dielectric substrate, by processing the metallization pattern along with the thickness and dielectric permittivity of the substrate. This approach harnesses the efficiency of convolutional neural networks with pattern recognition tasks and extends previous efforts to employ neural networks for the simulation of parameterized circuit geometries. Training is based on full-wave simulation data generated via the Finite-Difference Time-Domain (FDTD) method over a target frequency range. To accelerate the generation of such data, we build a hybrid neural network including recursive neural network modules, to compensate numerical dispersion errors in coarse-grid FDTD. This novel dispersion compensation scheme allows us to generate accurate FDTD training data from fast, coarse-grid simulations.

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Numerical Dispersion Compensation for FDTD via Deep Learning

Cited by 4 publications

References 7 publications

Accelerating finite-difference frequency-domain simulations of inverse designed structures in nanophotonics using deep learning

Accelerating finite-difference frequency-domain simulations of inverse designed structures in nanophotonics using deep learning

Super-Resolution Deep Learning Network for Finite-Difference Time-Domain Simulations

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

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