RDDA method for characterization of photonic nanojets

Area of Science:

• Photonics and Nanotechnology
• Computational Electromagnetics
• Optical Physics

Background:

• Photonic nanojets (PNJs) are highly confined light beams generated by microparticles.
• Accurate simulation of PNJs is crucial for applications in optical trapping, imaging, and sensing.
• Existing methods may require significant computational resources.

Purpose of the Study:

• To introduce and validate a reduced discrete dipole approximation (RDDA) method for PNJ simulation.
• To investigate the influence of microparticle shape and incident light polarization on PNJ characteristics.
• To identify optimal conditions for generating highly confined PNJs.

Main Methods:

• Developed a reduced discrete dipole approximation (RDDA) method.
• Simulated PNJs from spherical and ellipsoidal microparticles.
• Analyzed PNJ confinement using the quality factor (Q) based on incident beam polarization and filling factor.

Main Results:

• The RDDA method provides an efficient tool for PNJ field distribution computation.
• PNJ characteristics are sensitive to microparticle shape, incident polarization, and filling factor.
• Optimal PNJ confinement was achieved in microellipsoids with specific shape and filling factor parameters.

Conclusions:

• The RDDA method is a powerful and flexible approach for PNJ analysis.
• Microellipsoids offer superior PNJ confinement compared to spheres under optimized conditions.
• This work provides insights for designing microstructures for enhanced light focusing applications.