Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design

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.