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Achieving high diffraction efficiency in LCoS-SLMs via neural-network-based precise fringe-field compensation
Optics Express
|June 11, 2026
Summary
Researchers used a neural network to overcome limitations in Liquid Crystal on Silicon (LCoS) devices, achieving record deflection angles and diffraction efficiency for optical beam steering applications.
Area of Science:
- Optics and Photonics
- Materials Science
- Artificial Intelligence
Background:
- Liquid Crystal on Silicon (LCoS) devices are crucial for optical beam steering.
- Current LCoS technology faces challenges in simultaneously achieving large deflection angles and high diffraction efficiency due to the fringe-field effect.
Purpose of the Study:
- To develop a method for precise compensation of the fringe-field effect in LCoS devices.
- To enhance both deflection angle and diffraction efficiency for advanced optical applications.
Main Methods:
- A neural network was trained to precisely compensate for the fringe-field effect in LCoS devices.
- The trained neural network approach was experimentally validated using a commercial LCoS device.
Main Results:
- The proposed method achieved a diffraction efficiency exceeding 37.8% at 1550 nm.
- A continuous diffraction angle of up to 10° was demonstrated, representing a record for LCoS devices.
- The results show simultaneous achievement of high efficiency and large deflection angles.
Conclusions:
- The neural network-based fringe-field effect compensation significantly improves LCoS device performance.
- This breakthrough enables practical applications of LCoS in LiDAR, holography, and optical switches.
- The study sets a new benchmark for LCoS device capabilities in optical beam steering.
