Computationally Efficient Nanophotonic Design through Data-Driven Eigenmode Expansion
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911955457933312 |
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| author | Oktay, Mehmet Can Magden, Emir Salih |
| author_facet | Oktay, Mehmet Can Magden, Emir Salih |
| contents | Growing diversity and complexity of on-chip photonic applications requires rapid design of components with state-of-the-art operation metrics. Here, we demonstrate a highly flexible and efficient method for designing several classes of compact and low-loss integrated optical devices. By leveraging a data-driven approach, we represent devices in the form of cascaded eigenmode scattering matrices, through a data-driven eigenmode expansion method. We perform electromagnetic computations using parallel data processing techniques, demonstrating simulation of individual device responses in tens of milliseconds with physical accuracies matching 3D-FDTD. We then couple these simulations with nonlinear optimization algorithms to design silicon-based waveguide tapers, power splitters, and waveguide crossings with state-of-the-art performance and near-lossless operation. These three sets of devices highlight the broad computational efficiency of the design methodology shown, and the applicability of the demonstrated data-driven eigenmode expansion approach to a wide set of photonic design problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Nanophotonic Design through Data-Driven Eigenmode Expansion Oktay, Mehmet Can Magden, Emir Salih Optics Computational Physics Growing diversity and complexity of on-chip photonic applications requires rapid design of components with state-of-the-art operation metrics. Here, we demonstrate a highly flexible and efficient method for designing several classes of compact and low-loss integrated optical devices. By leveraging a data-driven approach, we represent devices in the form of cascaded eigenmode scattering matrices, through a data-driven eigenmode expansion method. We perform electromagnetic computations using parallel data processing techniques, demonstrating simulation of individual device responses in tens of milliseconds with physical accuracies matching 3D-FDTD. We then couple these simulations with nonlinear optimization algorithms to design silicon-based waveguide tapers, power splitters, and waveguide crossings with state-of-the-art performance and near-lossless operation. These three sets of devices highlight the broad computational efficiency of the design methodology shown, and the applicability of the demonstrated data-driven eigenmode expansion approach to a wide set of photonic design problems. |
| title | Computationally Efficient Nanophotonic Design through Data-Driven Eigenmode Expansion |
| topic | Optics Computational Physics |
| url | https://arxiv.org/abs/2407.09847 |