This paper describes the overview of special issue for laser processing technologies with femtoseccond pulsed laser. The topics of this issue cover surface nanostructuring, three-dimensional processing, and machine learning applications.

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Distinct characteristics of femtosecond lasers such as ultrashort pulse width and extremely high peak intensity have opened up new avenues for materials processing. Thus, many researchers are currently studying in diverse fields of materials processing using femtosecond lasers, including micromachining, surface nanostructuring, super-resolution nanofabrication, nano and novel materials synthesis, material welding and bonding, three-dimensional and volume processing, and tailored beam processing. Among these subjects, this paper reviews recent progress of three topics on surface nanostructuring, 3D and volume processing, and tailored beam processing that are being investigated by the author’s group.

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Spatially selective precipitation of crystal by femtosecond laser heating could add new functionality to specific region inside glasses, for example optical properties can be controlled electrically. Herein, we summarize the fundamental phenomena and examples of crystal precipitation and spatial element distribution change by femtosecond laser heating.

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Superimposed femtosecond laser pulses can produce periodic nanostructures on various kinds of materials. This surface phenomenon has attracted considerable interest both for applications in a laser nanoprocessing technique that exceeds the diffraction limit of light and for understanding unknown physical mechanisms. We studied the formation mechanism for nanostructuring and developed a control technique. In this paper, we review our experimental and theoretical nanostructuring results.

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The output of ultrafast laser has achieved kilowatts level for the average power due to the development of repetition rate. This revolution of laser average power makes ultrafast laser more useful with increased processing efficiency. In this paper, we introduced our work about surface modification of metals/ semiconductors substrate by using ultrafast laser processing. On silicon substrate surface, we successfully fabricated terahertz moth-eye structures and controlled their antireflective characteristics by adjusting their profiles. A super broadband hybrid antireflective structure has also been designed and achieved. Laser-induced surface cleavage, cleavage-melt shift, and formation of nano phosphor were observed when zinc oxide crystal was irradiated by ultrafast pulses. Patterning oxidation of copper substrate surface, carbonization of titanium alloys (Ti6Al4V), and fabrication of carbide nanoparticles, which achieved by using ultrafast laser processing, are also introduced.

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We describe fabrication and optical properties of perfect absorber electromagnetic metasurfaces working at infrared wavelengths. The metasurfaces consist of 3D metallic resonators which have helix and splitring geometry and are arranged into periodic arrays on a plane. In order to exploit functional features of these geometries and to realize metasurfaces operating at infrared wavelengths, unit cell size of these metasurfaces must be downscaled to a few micrometers, whereas the constituent helix and split-ring shaped features must be fabricated with sub-micrometric spatial resolution. We address the fabrication challenge by using femtosecond Direct Laser Write lithography technique to prepare dielectric templates of the metasurfaces and then converting them to metallic structures by using plasma sputtering of gold. The fabricated samples exhibit perfect absorption resonances at infrared wavelengths of 7 ~ 9 m, spectrally tunable by controlling their geometric parameters, and may find applications in narrow-band infrared detectors and emitters in the future.

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Machine learning, one of the methods in artificial intelligence, is computer algorithm for automatically improving by using the experience and/or the data. Recently, the study on the application of this algorithm is actively occurred in both CW and pulsed laser processing. This report provides an example of applying this algorithm to predict the ablation efficiency of femtosecond laser processing. For the work, five materials are selected: cold-rolled steel sheet and aluminum as the metal, Silicon wafer as the semiconductor, glass as the insulator, and polycrystalline diamond as the composite material. As the results, the ablation efficiencies of all materials were predicted with the accuracies of up to ±40%. In addition, the two application challenges of the learning model are introduced: (1) prediction of the ablation efficiency of one material using machine learning models from material properties and the other material processing results, and (2) influence of manufacturing errors on laser processing results.

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