Fast and accurate active alignment of camera lenses with physics-informed deep learning.

Area of Science:

• Optical Engineering
• Machine Learning
• Manufacturing Technology

Background:

• Complex optical systems demand precise alignment for optimal performance.
• Current active alignment (AA) methods face challenges in balancing speed and accuracy for large-scale manufacturing.
• Efficient alignment is crucial for high-volume production, such as in smartphone lens manufacturing.

Purpose of the Study:

• To develop a novel active alignment pipeline using physics-informed deep learning.
• To enhance the speed and accuracy of optical alignment in large-scale manufacturing.
• To address the limitations of existing AA techniques in balancing throughput and precision.

Main Methods:

• Proposed a two-component pipeline: a physics-informed tolerance estimation neural network (TolNet) and an optical optimization module.
• TolNet estimates tolerances from point spread functions (PSFs) using a hybrid data-driven and physics-driven loss strategy.
• The optical optimization module determines adjustment parameters for AA.

Main Results:

• The proposed physics-informed deep learning pipeline achieves exceptional speed for active alignment.
• TolNet completes tolerance estimation in under 0.01 seconds.
• The optical optimization module requires less than 3 seconds for adjustment parameter determination.
• Experimental validation confirmed the method's effectiveness in improving AA efficiency and accuracy.

Conclusions:

• The developed physics-informed deep learning pipeline offers a significant advancement for active alignment in complex optical systems.
• The method provides a promising solution for improving efficiency and accuracy in large-scale manufacturing environments.
• This approach effectively balances the speed and precision requirements for modern optical production.