Computer-aided alignment method based on deep learning

Wenxi Wang, Yifan Huang, Dongmei Li, Jiajing Cao, Xiaoxiao Lai, Jun Chang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The optical system will always be different from the design value after been processed. It is necessary to adjust the relative positions between the optical elements to improve the imaging quality of the system. However, if the elements are adjusted randomly, the alignment will be inefficient, so the computer-aided alignment method came into being. In this article, for the alignment of large aberration systems, a new fully-connected network computer-aided alignment (Fc-Net CAA) method is proposed. The systems’ wavefront errors (WFEs) are described by the Zernike polynomials which have a huge advantage in describing system aberrations and we proposed a Fc-Net model for predicting systems’ misalignment. The Fc-Net model is trained with the WFEs of thousands of randomly misaligned instances of the lens system that are modeled in the optical design software, so as to establish the relationship between the system aberrations and the amount of misalignment. In this way, the proposed Fc-Net CAA can achieve the computer-aided adjustment process for systems with large aberrations without a complicated iterative process. The off-axis three-mirror system with aspheric surfaces was simulated and adjusted. During the simulation, a single round of adjustment can make the optical system close to the design wave aberration values, and the average of the five field-of-view WFEs is enhanced from 2.4λ (RMS; λ=550nm) to 0.0764 λ (average). The simulation results verify that the improved algorithm can solve the large initial alignment error of the off-axis reflective optical system with aspheric surfaces.

Original languageEnglish
Title of host publicationAOPC 2022
Subtitle of host publicationNovel Optical Design; and Optics Ultra Precision Manufacturing and Testing
EditorsLingbao Kong, Dawei Zhang, Zexin Feng
PublisherSPIE
ISBN (Electronic)9781510662322
DOIs
Publication statusPublished - 2023
Event2022 Applied Optics and Photonics China: Novel Optical Design; and Optics Ultra Precision Manufacturing and Testing, AOPC 2022 - Virtual, Online, China
Duration: 18 Dec 202219 Dec 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12559
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2022 Applied Optics and Photonics China: Novel Optical Design; and Optics Ultra Precision Manufacturing and Testing, AOPC 2022
Country/TerritoryChina
CityVirtual, Online
Period18/12/2219/12/22

Keywords

  • Computer-aided alignment
  • Fully-Connected network
  • Zernike polynomial
  • deep learning

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