Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems

2025-06-29

AI Summary

Optical multi-lens system alignment accuracy critically affects imaging performance; however, conventional alignment methods relying on specialized metrology equipment suffer from low efficiency and poor scalability. This paper proposes a marker-free, purely optical misalignment diagnosis method based on deep learning. It employs a dual-modality input—integrating ray-tracing–generated spot patterns with physically simulated grayscale images—and introduces two complementary inverse neural network architectures to jointly model 5-DOF (for six-mirror systems) or 4-DOF (for two-mirror systems) misalignment errors. The approach requires no prior structural parameters or mechanical sensors, enabling end-to-end error prediction from a single optical measurement. Experiments demonstrate high accuracy: mean absolute error (MAE) of 0.031 mm for lateral translation and 0.011° for tilt in the six-mirror configuration. Both two- and six-mirror systems confirm strong robustness and cross-configuration generalization capability, significantly enhancing automation and efficiency in quality inspection for precision imaging systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the rapidly evolving field of optical engineering, precise alignment of multi-lens imaging systems is critical yet challenging, as even minor misalignments can significantly degrade performance. Traditional alignment methods rely on specialized equipment and are time-consuming processes, highlighting the need for automated and scalable solutions. We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems using only optical measurements. First, we use ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime, achieving a mean absolute error of 0.031mm in lateral translation and 0.011$^circ$ in tilt. We also introduce a physics-based simulation pipeline that utilizes grayscale synthetic camera images, enabling a deep learning model to estimate 4-DOF, decenter and tilt errors in both two- and six-lens multi-lens systems. These results show the potential to reshape manufacturing and quality control in precision imaging.

Problem

• Diagnosing misalignments in multi-lens imaging systems
• Automating alignment processes with deep learning
• Improving precision in manufacturing and quality control

Innovation

• Deep learning for optical misalignment diagnostics
• Ray-traced spot diagrams predict 5-DOF errors
• Physics-based simulation with synthetic images