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Adaptive beam forming across temperature variation in optical phased array enabled with deep neural network.
Optics Express
|June 11, 2024
Summary
A deep neural network (DNN) adaptively controls optical phased arrays (OPAs) to maintain beam patterns despite manufacturing variations and temperature shifts. This technology enables precise beam forming for advanced photonic applications.
Area of Science:
- Photonics
- Optical Engineering
- Machine Learning
Background:
- Integrated optical phased arrays (OPAs) require calibration due to channel mismatches.
- OPA beam patterns are susceptible to distortion from temperature fluctuations.
Purpose of the Study:
- To develop a deep neural network (DNN) for adaptive control of OPAs.
- To compensate for process mismatches and temperature variations in OPA beam forming.
Main Methods:
- Implemented a DNN to control phase modulator voltages in a 128-channel OPA.
- Utilized a commercial silicon photonics (SiP) process for OPA fabrication.
- Demonstrated adaptive beam forming and multi-peak beam generation.
Main Results:
- Achieved accurate beam forming within a 50° field of view.
- Demonstrated 0.025° accuracy with 0.1° beam sweeping at a fixed temperature.
- Maintained beam integrity across a 20°C temperature range.
Conclusions:
- DNN-based adaptive control effectively addresses OPA calibration and temperature stability issues.
- The proposed method enables robust and precise beam steering for integrated photonic systems.
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