Electrooptic mapping of near-field distributions in integrated microwave circuits

1998 ◽  
Vol 46 (12) ◽  
pp. 2338-2343 ◽  
Author(s):  
K. Yang ◽  
G. David ◽  
S.V. Robertson ◽  
J.F. Whitaker ◽  
L.P.B. Katehi
Keyword(s):  
Near Field ◽  
Microwave Circuits ◽  
Field Distributions

scholarly journals Mapping Magnetic Near-Field Distributions of Plasmonic Nanoantennas

2013 ◽  
Author(s):  
Denitza Denkova ◽  
Niels Verellen ◽  
Alejandro V. Silhanek ◽  
Ventsislav K. Valev ◽  
Pol Van Dorpe ◽  
...  
Keyword(s):  
Near Field ◽  
Field Distributions ◽  
Plasmonic Nanoantennas

scholarly journals Broadband corner cloak using a uniaxial transformation medium of stacked artificial dielectric sheets

2020 ◽  
Vol 7 ◽  
pp. 4
Author(s):  
Yuma Takano ◽  
Atsushi Sanada
Keyword(s):  
Cross Sections ◽  
Near Field ◽  
Field Measurements ◽  
Corner Reflector ◽  
Specular Scattering ◽  
Transformation Medium ◽  
Field Distributions ◽  
Artificial Dielectric ◽  
Measured Field ◽  
Original Area

We demonstrate corner cloak operations mimicking a corner reflector and hiding objects in a truncated corner. The corner cloak is designed at 18.25 GHz and implemented by nonresonant artificial dielectric sheets stacked onto the bottom hypotenuse. It is shown by the near-field measurements that the measured field distributions for the cloak agree well with those for the original area of the corner reflector as well as those for the numerical prediction. The bistatic radar cross-sections (BRCSs) for the cloak and the original area calculated from the measured field distributions coincide with each other and the cloak operation is quantitatively confirmed. The bandwidth evaluated by the specular scattering angles from the BRCSs shows broadband operation as wide as from 16 to 22 GHz.

Physics-augmented deep learning for high-speed electromagnetic simulation and optimization

2021 ◽  
Author(s):  
Mingkun Chen ◽  
Robert Lupoiu ◽  
Chenkai Mao ◽  
Der-Han Huang ◽  
Jiaqi Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Electromagnetic Field ◽  
Electric Fields ◽  
High Speed ◽  
Near Field ◽  
Physical Constraints ◽  
Simulation And Optimization ◽  
Field Distributions ◽  
The Neural Network ◽  
Nanostructure Arrays

Abstract The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. We introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds and high accuracy for entire classes of dielectric photonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: as physical constraints in the loss function and as a means to calculate the electric fields from the magnetic fields. As a model system, we construct a surrogate simulator for periodic silicon nanostructure arrays and show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings. We anticipate that physics-augmented networks will serve as a viable Maxwell simulator replacement for many classes of photonic systems, transforming the way they are designed.

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