.png){kind=icon}
Return
Computationally Efficient Nanophotonic Design Through Data-Driven Eigenmode Expansion
.png){kind=icon}
OA
AI
DOI：10.1109/JLT.2024.3430852.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Optical waveguides
Transmission line matrix methods
Databases
Performance evaluation
Scattering
Geometry
Photonics
Eigenmode expansion
integrated photonics
power splitters
waveguide crossings
waveguide tapers
Journal
IF:
4.8
Papers:
1.7W
Citations:
3.8W
Organization
Cited Papers
ShareSaveInverse design of high-dimensional quantum optical circuits in a complex medium.png)
NATURE PHYSICSOAAI
NATURE PHYSICSIF18.4
OAAIShareSaveInteracciones entre índice de área foliar, densidad del dosel y precipitación efectiva de un bosque de Polylepis reticulata ubicado en un ecosistema de páramoLa GranjaOAAI
La GranjaIF0
OAAIShareSaveShareSave
View more