TR2022-047

Finding the Right Deep Neural Network Model for Efficient Design of Tunable Nanophotonic Devices


Abstract:

We develop generative deep neural networks that explore relevant statistical structures to expedite a complex inverse design of nanophotonic on-chip wavelength demultiplexer. Our design, targeting at telecomm-wavelengths, is electrically switchable via liquid crystal tuning.

 

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