Deep learning for optical misalignment diagnostics in multi-lens imaging systems.

Area of Science:

• Optical engineering
• Machine learning
• Image processing

Background:

• Precise alignment of multi-lens imaging systems is crucial for performance.
• Traditional alignment methods are time-consuming and require specialized equipment.
• There is a need for automated and scalable solutions for misalignment diagnosis.

Purpose of the Study:

• To present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems.
• To enable misalignment diagnosis using only optical measurements.
• To improve manufacturing and quality control in precision imaging.

Main Methods:

• Utilizing ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime.
• Developing a physics-based simulation pipeline with grayscale synthetic camera images.
• Employing deep learning models to estimate decenter and tilt errors (4-DOF) in two- and six-lens systems.

Main Results:

• Achieved a mean absolute error of 0.031 mm in lateral translation and 0.011° in tilt for a 6-lens system using spot diagrams.
• Successfully estimated 4-DOF, decenter, and tilt errors in both two- and six-lens systems using synthetic camera images.
• Demonstrated the efficacy of deep learning for automated misalignment diagnosis.

Conclusions:

• Deep learning-based inverse-design offers a promising automated solution for diagnosing lens system misalignments.
• These methods can significantly enhance precision and efficiency in optical manufacturing and quality control.
• The presented techniques have the potential to reshape the field of precision imaging.