Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas.

Area of Science:

• Photonics and Nanotechnology
• Computational Electromagnetics
• Materials Science

Background:

• Rational design of photonic nanostructures is challenged by multi-objective optimization.
• Traditional methods require complex computational schemes for simultaneous optical property tuning.
• Evolutionary algorithms offer a nature-inspired approach to optimize complex designs.

Purpose of the Study:

• To develop a numerical technique for designing photonic nanostructures with multiple, arbitrary optical objectives.
• To optimize silicon nanostructure-based color pixels for specific, polarization-dependent resonant wavelengths.
• To demonstrate a self-adaptive design method for complex nanophotonic structures.

Main Methods:

• Combining evolutionary multi-objective algorithms (EMOAs) with frequency-domain electrodynamical simulations.
• Utilizing EMOAs to iteratively refine nanostructure morphology based on selection, mutation, and cross-over.
• Fabricating optimized silicon nanostructures using electron-beam lithography.

Main Results:

• Successfully designed photonic nanostructures (color pixels) with tailored optical properties.
• Achieved resonance at two user-defined, polarization-dependent wavelengths.
• Experimental scattering spectra showed excellent agreement with simulation-predicted objectives.

Conclusions:

• The presented numerical technique effectively designs photonic nanostructures for multiple optical objectives.
• The method is adaptable to various constraints, suitable for complex nanophotonic device fabrication.
• This approach advances the design of advanced optical materials and devices.