Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems
This addresses the need for automated and scalable alignment diagnostics in optical engineering, potentially reshaping manufacturing and quality control for precision imaging systems.
The paper tackled the problem of diagnosing misalignments in multi-lens imaging systems by developing deep learning-based inverse-design methods using optical measurements, achieving a mean absolute error of 0.031mm in lateral translation and 0.011° in tilt for a 6-lens system.
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.