Journal
MICROELECTRONIC ENGINEERING
Volume 227, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mee.2020.111314
Keywords
CGH; Laser materials processing; Regression; Machine learning; Image processing; Gabor features
Categories
Funding
- Operational Programme Competitiveness, Entrepreneurship and Innovation (NSRF) [MIS 5002735]
- European Union (European Regional Development Fund)
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Fabrication of Computer-Generated Holograms (CGHs) on metal surfaces is a challenging procedure, given the nature of the laser-matter interaction specified for metals, and the power requirements for silver laser machining. A machine learning approach is derived for engraving of CGHs on silver surfaces with a 1070 nm fiber laser. The proposed method paves the way towards an automated solution for the fabrication of CGH on silver surfaces that accounts for, in terms of manufacturability. Sophisticated image-based descriptors are extracted from digital holographic masks produced by commercial CGH design software to predict, using machine learning, a quality score from '1' to '5', estimating the fabrication feasibility of a CGH's mask. Based on this idea, the procedure of CGH engraving on silver is remarkably improved.
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