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Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design.

Qianyi Wu1, Yihao Xu2, Junxiang Zhao1

  • 1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.

Nano Letters
|September 5, 2024
PubMed
Summary

We developed a hybrid AI framework to optimize plasmonic nanoantenna arrays for super-resolution microscopy. This approach accelerates the design of localized plasmonic structured illumination microscopy (LPSIM) for advanced biological imaging.

Keywords:
Deep learningGenetic algorithmsPhotonics inverse designPlasmonicsStructured illumination microscopySuper-resolution microscopy

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Area of Science:

  • Optics and Photonics
  • Biomedical Imaging
  • Nanotechnology

Background:

  • Super-resolution fluorescence imaging provides critical biological insights.
  • Localized plasmonic structured illumination microscopy (LPSIM) offers video-rate imaging at ~50 nm resolution using plasmonic nanoantenna arrays.
  • Traditional LPSIM array design is inefficient, hindering optimization.

Purpose of the Study:

  • To introduce a hybrid inverse design framework combining deep learning and genetic algorithms for LPSIM array optimization.
  • To demonstrate a more efficient and effective method for designing plasmonic substrates for super-resolution microscopy.

Main Methods:

  • A deep learning model (convolutional neural network) was trained to evaluate LPSIM array designs.
  • Genetic algorithms and multiobjective optimization were employed to iteratively refine and evolve array designs.
  • Simulations were used to compare optimized and traditional LPSIM substrates.

Main Results:

  • Optimized LPSIM substrates showed superior performance compared to traditional designs.
  • Key improvements included higher reconstruction accuracy and enhanced robustness against noise.
  • The optimized substrates demonstrated increased tolerance for fewer measurements.

Conclusions:

  • The hybrid inverse design framework effectively tailors LPSIM substrates for improved performance.
  • This AI-driven approach accelerates the discovery of novel plasmonic nanostructures for advanced imaging.
  • The framework opens new possibilities for nanophotonics in biological and imaging applications.