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Optimization of the silicon-based aperiodic optical phased array antenna
Optics Letters
|February 12, 2021
Summary
This study optimizes silicon optical phased arrays (OPAs) for LiDAR using a genetic algorithm to reduce unwanted sidelobes. The results show a unique mainlobe behavior in aperiodic arrays, offering a new pathway for OPA optimization.
Area of Science:
- Photonics and Optical Engineering
- Machine Learning Applications
- Materials Science
Background:
- Beam engineering is crucial for Light Detection and Ranging (LiDAR) systems.
- Optical Phased Arrays (OPAs) offer precise beam control capabilities.
- Aperiodic array designs present unique challenges and opportunities in beam shaping.
Purpose of the Study:
- To optimize the beam profile of silicon optical phased arrays (OPAs) for enhanced LiDAR performance.
- To suppress sidelobes in the far-field radiation pattern of aperiodic OPAs.
- To investigate the relationship between mainlobe position and wavelength in optimized 2D aperiodic arrays.
Main Methods:
- Utilized a machine-learning-based genetic algorithm for optimization.
- Simulated and analyzed aperiodic array distributions.
- Investigated the impact of fabrication errors on side mode suppression ratio.
Main Results:
- Successfully suppressed sidelobes in the far-field pattern of aperiodic arrays.
- Observed a distinct mainlobe position versus wavelength relationship, differing from previous studies.
- Quantified the effect of fabrication errors on the side mode suppression ratio.
Conclusions:
- Developed an effective method for optimizing random distributed OPAs within a practical timeframe.
- The findings provide a pathway for improved beam control in LiDAR and other optical systems.
- The study highlights the potential of machine learning in advanced optical array design.
