Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems
Journal:
arXiv
Published Date:
Jun 29, 2025
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.
