Chromatic confocal microscopy with galvanometer scanning and CNN-based deconvolution for precise full-field surface profilometry

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.