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Nanophotonic structure inverse design for switching application using deep learning.

Ehsan Adibnia1, Majid Ghadrdan1, Mohammad Ali Mansouri-Birjandi2

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Summary

Deep learning accelerates the design of nanoscale all-optical plasmonic switches. This method efficiently solves inverse design problems, enabling faster communication systems and photonic integrated circuits.

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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.