Chromatic confocal microscopy with galvanometer scanning and CNN-based deconvolution for precise full-field surface profilometry
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Journal Volume
13704
Start Page
137040I
ISSN
0277786X
ISBN (of the container)
9781510693579
ISBN
9781510693579
Date Issued
2025
Author(s)
Abstract
This study presents a full-field chromatic confocal microscopy (CCM) system with galvanometer scanning and CNN-based deconvolution. A programmable liquid crystal on silicon (LCoS) replaces the spectrometer slit, enabling adaptive filtering. Finite Difference Time Domain (FDTD) and Zernike-based simulations are employed to generate training data. The deep learning model enhances lateral resolution, achieving a 32-fold reduction in bias compared to scanning probe microscopy (SPM) in microbump width.
Event(s)
Optical Technology and Measurement for Industrial Applications Conference 2025, Yokohama, 21 April 2025 - 25 April 2025
Subjects
Chromatic confocal microscopy (CCM)
convolutional neural network (CNN)
deep learning
full-field surface profilometry
optical metrology
Publisher
SPIE
Type
conference paper