Nanophotonic structure inverse design for switching application using deep learning.

Area of Science:

• Photonics and Nanotechnology
• Computational Electromagnetics

Background:

• Traditional optical switch design relies on iterative simulations and Maxwell's equations.
• Inverse design problems for nanophotonic devices are computationally intensive and time-consuming.

Purpose of the Study:

• To propose a deep neural network (DNN)-based method for approximating spectral transmittance of all-optical switches.
• To demonstrate the efficacy of deep learning in solving nanophotonic inverse design problems.

Main Methods:

• A deep neural network model was developed to predict the spectral transmittance of all-optical switches.
• The nonlinear Kerr effect in square resonators was utilized to showcase switching performance.
• The DNN model was trained and validated against conventional simulation methods.

Main Results:

• The DNN model achieved high accuracy, with mean squared errors of approximately 0.03 for forward and 0.02 for inverse models.
• The deep learning approach significantly improved computational efficiency compared to traditional simulations.
• The proposed method effectively resolved nanophotonic inverse design challenges without empirical strategies.

Conclusions:

• Deep learning offers a powerful and efficient tool for designing all-optical plasmonic switches.
• The developed method facilitates the integration of advanced optical switches into photonic integrated circuits.
• This advancement holds significant potential for the progression of all-optical signal processing and communication systems.