Computer-aided alignment method based on deep learning

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

doi:10.1117/12.2648166

20 January 2023 Computer-aided alignment method based on deep learning

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

Proceedings Volume 12559, AOPC 2022: Novel Optical Design; and Optics Ultra Precision Manufacturing and Testing; 1255906 (2023) https://doi.org/10.1117/12.2648166
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

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 offaxis reflective optical system with aspheric surfaces.

Citation Download Citation

Wenxi Wang, Yifan Huang, Dongmei Li, Jiajing Cao, Xiaoxiao Lai, and Jun Chang "Computer-aided alignment method based on deep learning", Proc. SPIE 12559, AOPC 2022: Novel Optical Design; and Optics Ultra Precision Manufacturing and Testing, 1255906 (20 January 2023); https://doi.org/10.1117/12.2648166

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Optical alignment

Neural networks

Systems modeling

Computer aided design

Data modeling

Tolerancing

Imaging systems

Keywords/Phrases

Search In:

Publication Years