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

Tomer Slor; Dean Oren; Shira Baneth; Tom Coen; Haim Suchowski

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arXiv · 2506.23173

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

Tomer Slor·Dean Oren·Shira Baneth·Tom Coen·Haim Suchowski

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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.

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Tomer Slor, Dean Oren, Shira Baneth, Tom Coen, Haim Suchowski. 2025-06-29. Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems. https://arxiv.org/abs/2506.23173

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