Reconfigurable Wavelength Selective Splitter Design
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for designing materials and devices face challenges in optimizing multiple parameters simultaneously due to the time-consuming nature of simulations and experiments, and existing inverse neural networks are limited in handling more complex optimization problems, often resulting in narrower bandwidth and semi-optimized results.
Innovation Solution
A conditional variational autoencoder combined with an adversarial network is used to randomly generate device designs, employing active training and materials like silicon, silicon dioxide, and liquid crystal, which allows for more sophisticated optimization by modeling the probability distribution of data and sampling new data from it, utilizing FDTD simulations and adjoint optimization methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional simulation or experiment methods are used to verify updated characteristics for new parameter sets, then design accuracy can be ensured, but design time increases significantly
Solution Approach 1:
The patent pre-trains neural network models using extensive simulation data before actual device design. This preliminary training phase creates a ready-to-use inverse design model that can rapidly generate design parameters without requiring time-consuming simulations during the actual design process, thus ensuring both accuracy and speed.
Solution Approach 2:
The patent uses neural networks to create virtual copies of the complex simulation process. Instead of running actual physics simulations for each design iteration, the trained network model generates design parameters that replicate the outcomes of full simulations, dramatically reducing computation time while maintaining design accuracy.
2Device complexity
If binary structure optimization is used in inverse neural network models, then the optimization problem dimension is reduced, but the bandwidth narrows and results become semi-optimized
Solution Approach 1:
The patent applies different structural representations to different parts of the device design. Continuous parameters are used for regions requiring fine-tuned optimization (like waveguide dimensions), while binary or discrete parameters are used for structural topology decisions. This hybrid approach maintains both computational efficiency and design flexibility across different device regions.
Solution Approach 2:
The patent transitions from static binary structure optimization to dynamic continuous parameter optimization. The neural network model handles continuous parameters that can vary smoothly, enabling broader bandwidth optimization and more sophisticated device designs while maintaining computational tractability through the learned prior distributions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient generation of device layouts with improved performance, achieving high transmission efficiency across a broad bandwidth, significantly reducing design time and improving optimization results compared to conventional methods.
Implementation Method 1
Liquid crystal has anisotropic refractive index, and by applying electric field, the axis of oriental can be changed, along with the refractive index in each direction.
Implementation Method 2
by applying electric field, the axis of oriental can be changed, along with the refractive index in each direction.
Data Source
AI summary
A reconfigurable device is provided for splitting optical beams. The device includes an input port configured to receive an input beam including at least two primary wavelengths, a tunable splitter configured to separate the input beam into at least two beams via at least two routes corresponding to the at least two primary wavelengths, wherein each of the at least two routes is configured to propagate one of the at least two primary wavelengths of the input beam, wherein the tunable splitter includes a bottom electrode, a substrate on the bottom electrode, core segments arranged on the substrate, a top layer, support segments to connect the substrate and the top layer, a top electrode on the top layer, a controllable refractive index layer arranged to fill gaps between the substrate, the support segments, and the top layer; and at least two output ports configured to transmit the at least two beams propagated via the at least two routes.