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

TL;DR

This paper introduces deep learning methods to automatically diagnose misalignments in multi-lens imaging systems using optical measurements, significantly improving speed and accuracy over traditional techniques.

Contribution

It presents two novel deep learning-based inverse-design approaches for diagnosing lens misalignments from optical data, including ray-traced diagrams and synthetic images.

Findings

Achieved 0.031mm accuracy in lateral translation prediction.

Achieved 0.011° accuracy in tilt error estimation.

Demonstrated effectiveness in both 2- and 6-lens systems.

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.