CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13241 > Article
Paper
20 November 2024 A deep learning-based trajectory correction method for two-dimensional galvanometer scanners
Yuhui Chu, Sicheng Li, Huabing Tan, Pengpeng Zhao
Author Affiliations +
Proceedings Volume 13241, Optical Metrology and Inspection for Industrial Applications XI; 132410E (2024) https://doi.org/10.1117/12.3033757
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China
Abstract
Two-dimensional galvanometer scanners are critical instruments in optical scanning systems. However, the scanning trajectories of galvanometer scanners are susceptible to distortions caused by mechanical and electrical imperfections, which inevitably compromise optical scanning performance. While closed-loop feedback signals can help mitigate these distortions, their accuracy is often restricted by perturbation during prolonged high-speed operation. In this study, we propose a deep learning-based trajectory correction method to achieve high-performance optical scanning in galvanometer scanners. By integrating a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM), the hybrid architecture effectively reduced trajectory errors by over 97% across three driving configurations. Furthermore, the optical performance was assessed by imaging three different patterns using the corrected trajectories, revealing a substantial improvement in image quality compared to those reconstructed from the original uncorrected trajectories.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Yuhui Chu, Sicheng Li, Huabing Tan, and Pengpeng Zhao "A deep learning-based trajectory correction method for two-dimensional galvanometer scanners", Proc. SPIE 13241, Optical Metrology and Inspection for Industrial Applications XI, 132410E (20 November 2024); https://doi.org/10.1117/12.3033757
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Galvanometers
Feedback signals
Deep learning
Image quality
Optical scanning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top