Machine learning based technique towards smart laser fabrication of CGH

被引：4

作者：

Anastasiou, Aggeliki ^{[1
]}

Zacharaki, Evangelia, I ^{[2
]}

Alexandropoulos, Dimitris ^{[1
]}

Moustakas, Konstantinos ^{[2
]}

Vainos, Nikolaos A. ^{[1
]}

机构：

[1] Univ Patras, Dept Mat Sci, Rion, Patra, Greece

[2] Univ Patras, Dept Elect & Comp Engn, Rion, Patra, Greece

来源：

MICROELECTRONIC ENGINEERING | 2020年 / 227卷

关键词：

CGH; Laser materials processing; Regression; Machine learning; Image processing; Gabor features; COMPUTER-GENERATED HOLOGRAMS; MARKINGS; TRANSFORM; SURFACE;

D O I：

10.1016/j.mee.2020.111314

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：6

共 36 条

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