Fast focusing of electrowetting liquid lenses driven by model-based feedforward voltage waveforms

Area of Science:

• Optics and Photonics
• Control Systems Engineering
• Materials Science

Background:

• Electrowetting-based liquid lenses (EWLLs) are crucial for adaptive optics, enabling rapid zooming.
• Improving the dynamic response of EWLLs is key for faster adaptability in optical systems.
• Current methods for adjusting driving voltage waveforms (DVWs) lack automatic calibration and pre-tuning.

Purpose of the Study:

• To develop a dynamic model-based feedforward controller for generating adaptive DVWs for EWLLs.
• To enable fast focusing of EWLLs without manual pre-tuning or calibration.
• To significantly improve the dynamic response and adaptability of EWLLs in optical systems.

Main Methods:

• Derived a dynamic model of EWLLs from the Onsager variational principle to describe liquid-interface motion under varying voltages.
• Validated the model experimentally for accurate voltage-motion relationship formulation.
• Designed an inverse-dynamics feedforward controller utilizing the dynamic model to generate adaptive DVWs for arbitrary optical power trajectories.

Main Results:

• The dynamic model accurately predicts the voltage-motion relationship of EWLLs.
• The proposed controller reduced response time by 63% for step changes in optical power compared to conventional methods.
• Consistent performance was observed across the entire voltage range for random optical power changes, eliminating the need for case-by-case tuning or calibration.

Conclusions:

• The dynamic model-based feedforward controller effectively generates adaptive DVWs for fast EWLL focusing.
• This approach significantly enhances the dynamic response of EWLLs, enabling faster adaptability in optical systems.
• The method offers a robust, automatic solution for EWLL control, removing the need for manual calibration and sensor feedback.