Fractional Fresnel coefficients for optical absorption in femtosecond laser-induced rough metal surfaces

Fractional Fresnel coefficients for optical absorption in femtosecond laser-induced rough metal surfaces

Muhammad Zubair Electrical Engineering Department, Information Technology University of the Punjab, Lahore, Pakistan 54600
SUTD-MIT International Design Center, Singapore University of Technology and Design, Singapore 487372
   Yee Sin Ang    Kelvin J. A. Ooi    L. K. Ang ricky$˙$ Science and Math & SUTD-MIT International Design Center, Singapore University of Technology and Design, Singapore 487372

The surface morphology of metal influences its optical absorptivity. Recent experiments have demonstrated that the femtosecond laser induced surface structures on metals could be dynamically controlled by the fluence of laser and the number of pulses. In this paper, we formulate an analytical model to calculate the optical absorption of a rough metallic surface by modeling the roughness as a fractal slab. For a given experimental image of the surface roughness, we characterize the roughness with a fractal parameter by using box-counting method. With this parameter as input, we calculate the absorption of 800 nm laser pulse impinging on gold, copper and platinum, and the calculated results show excellent agreements. In terms of physics, our model can be viewed as a fractional version of the Fresnel coefficients, and it will be useful for designing suitable surface structures to tune the light absorption on metals from purely reflective to highly absorptive based on different applications.

preprint: APS

I Introduction

Metal is a highly reflective material, however it is important to increase its optical absorption for some specific applications  Gamaly (2011); Vorobyev and Guo (2013). It was demonstrated that the optical absorption of metals can be significantly enhanced by using femtosecond (fs) laser induced surface structures  Vorobyev and Guo (2005); Vorobyev et al. (2009). As an example, for gold, the absorption can be increased from its intrinsic value of a few percent to nearly perfect absorption due to different laser-induced surface roughness  Vorobyev and Guo (2005). The temporal and spatial evolution of this type of fs laser-induced roughness on metals by utilizing a time-resolved optical imaging technique has been obtained  Fang et al. (2017). Under multiple pulses condition, nano-scale and micro-scale structures are respectively, formed at lower and higher laser fluence. Compared to other techniques, the fs laser pulse processing technique has received attention over the past decades owing to its ability to apply on multi-scale non-planar surfaces of metals Sugioka and Cheng (2014).

Depending on the size of roughness, which can be either smaller or larger than laser wavelength (), the physics of the enhanced absorption of laser can be due to different mechanisms. For roughness smaller than , the light absorption can be due to the antireflection effect of random sub-wavelength surface textures in terms of a graded refractive index at the air-metal interface Ghmari et al. (2004). For roughness greater than , the enhancement can be due to multiple reflections within the large surface structures Ang et al. (1997). The optical properties of a geometrical modified nano-materials is also different from its bulk counterpart Gavrilenko (2011).

Figure 1: (a) Diagram of laser absorption on a rough metal surface. (b) Effective model, where the air-metal interface is treated as a slab of thickness in a fractional-dimension-space () and the value of measures the effect of roughness. The value of is determined by having the calculation at = 1 (zero roughness) equal to the intrinsic absorption value or experimental measurement.

It is computationally expensive to apply full-wave numerical simulation to calculate the absorption of light over a wide range of roughness scales. In the existing analytical or semi-analytical models Ang et al. (1997); Bergström (2008), the characterization of roughness and surface morphology is approximated by various profilometric measurements and mathematical models, which are expressed as a function of various surface roughness parameters, such as depth (), width () and its ratio of . Given the wide range of roughness (from nano- to micro- and macro-scale), the prior models may not be sufficient to describe the complexity of a rough surface. Our analysis on several SEM images of such rough surfaces has shown that the surface roughness can be better characterized as fractals Mandelbrot and Pignoni (1983) (see Supplementary Information). The fractal description is insensitive to structural scale and provides a useful parameter to characterize the surface morphology, which is termed as the fractal dimension Falconer (2004). The fractal-based model is also independent of the resolution of the characterization Risović et al. (2009). It has been shown statistically that the fractal dimension is one of the most relevant parameters in surface morphology characterization Risović et al. (2008) that can also be correlated with various other surface roughness parameters Russ (2001).

In this paper, we develop an analytical fractional model to calculate the absorption of a laser pulse impinging on a rough metal, where the effect of roughness is treated by using a fractal parameter called (). By solving the corresponding Maxwell equations with appropriate boundary conditions in a fractional model (see below), we obtain an analytical form of the fractional Fresnel coefficients. By analyzing the SEM images of laser-induced rough metal surface structures from various experiments of different metals  Vorobyev and Guo (2007a, 2005, b), we mathematically determine the values of , and use them to calculate the optical absorption, which agree very well with experimental measurement. At = 1, the model will reproduce the absorption for a perfect surface with zero roughness. It is important to note that our model provide (for the first time) the analytical solutions to relate directly the laser absorption to the roughness of a metal surface. The proposed model is useful in the design of different surface structures on any metal surfaces so one can tune the optical absorption on a metallic surface in many applications such as photonics, plasmonics, optoelectronics, optofluidics, stealth technology, airborne devices, solar energy absorbers, and thermo-photovoltaics (see Vorobyev and Guo (2013); Sugioka and Cheng (2014); Fang et al. (2017); and reference therein).

Ii Fractional Model of Optical Absorption

Fractional-dimensional approach has attracted widespread attention in recent years motivated by its fundamental importance and possible practical applications in electromagnetic modeling of complex, anisotropic, heterogeneous, disordered or fractal media Zubair et al. (2012); Tarasov (2014). This approach has been applied in various areas of physics and engineering including the quantum field theory Stillinger (1977); Palmer and Stavrinou (2004), general relativity Sadallah and Muslih (2009), thermodynamics Tarasov (2016), mechanics Ostoja-Starzewski et al. (2014), hydrodynamics Balankin and Elizarraraz (2012), electrodynamics Tarasov (2015); Zubair et al. (2012); Mughal and Zubair (2011); Naqvi and Zubair (2016); Zubair et al. (2011a); Asad et al. (2012a, b); Zubair et al. (2011b, c, d, 2010), and fractional charge transport Zubair and Ang (2016); Zubair et al. (2018a, b) to name a few.

The proposed model is based on the formulation of Maxwell equations in fractional-dimensional spaces (see Zubair et al. (2012) and references therein) to calculate the effective Fresnel coefficients for a generalized rough surface as shown in Fig. (1). The rough metal surface is modeled as a slab of thickness from to , where we have air and metal, respectively, in region (above) and (below). For an interface at , we define two fractional-dimensional (FD) regions, with fractal dimensions and . The region of , it has a dimension of for a medium with parameters , and . Similarly, for , we have dimension for a medium with parameters and . For general formulation, we keep the expressions of , , , and , instead of spelling out the medium as air or metal specifically. In this simplistic fractional model, it is considered that the surface roughness alters the effective EM field in the direction normal to the interface and hence only the normal coordinate is fractionalized to achieve an equivalent geometry shown in Fig. (1). It is to be noted that this model does not provide exact field distribution at the rough surface locally, however, an approximate value of variation in absorption can be simply achieved in an effective manner. Hence, under the assumption that both regions are fractional dimensions only in the direction normal to the interface (-axis), we may write , where both and are the corresponding fractal dimensions.

Considering a linearly polarized transverse magnetic (TM) or p-polarized plane wave with an electric-field strength incident at . The incident, reflected and transmitted electric fields are given, respectively, as




Here, is the corresponding wave number of each half space, is the Hankel function of type 2 with order . The angle of incidence, reflection and transmission are , and . Similarly, the corresponding magnetic fields are




Here, we have and the wave impedance is .

The transmission () and reflection () coefficients are obtained by equating the tangential components of the electric and magnetic fields at the interfaces. By using the Snell’s law of , we have


By carrying out the same procedure, for transverse electric (TE) polarization or s-polarization, we get


As mentioned before, the absorption at a rough metal interface is calculated effectively by using a thin slab of fractional dimension and of thickness as shown in Fig. (1). The region 1 () is air, the region 2 () is a fractional slab (), and the region 3 () corresponds to metal characterized by complex permittivity . The transmission and reflection coefficients for interface at are represented as and , which can be both calculated using the expressions of and given in Eq. (13) through Eq. (16) by setting appropriate input parameters (, ) for a given polarization and the corresponding and . Similarly, we can calculate , , respectively, at .

Using the calculated , , , , the overall reflectivity and absorptivity become  Born and Wolf (2013)


Note the value of is determined by matching the calculated absorption at = 1 (zero roughness) to the theoretical value or experimental measurement for a flat surface.

Iii Results and Discussions

Figure (2) shows the calculated absorptivity from Eq. (18) for s and p-polarized laser radiation of =690nm imping on a solid aluminium target as a function of incidence angle for different fractional parameter = 0.1 to 1. From the figure, we see the absorption increases with roughness when is reduced from 1 to 0.1. The limit of corresponds to a perfectly smooth surface with an absorption of around 2 percent.

Figure 2: Absorption of laser radiation of =690nm on a solid aluminium target for (a) p-polarization, (b) s-polarization.

To compare with experimental results, we analyze the reported experimental absorption data of fs laser-induced roughness on 3 metals: copper Vorobyev and Guo (2007a), gold Vorobyev and Guo (2005) and platinum Vorobyev and Guo (2007b), all under 800 nm laser incidence at zero incidence. From the experiments, the surface structures were produced by varying laser fluence () and by using different number of laser pulses (). For comparison, we first determine the surface fractal-dimension from the published SEM images by using box-counting method Moisy (2008); Davis et al. (2017); Chen et al. (2017) to determine the dimension of a fractal object  Falconer (2004).

Figure 3: (a) SEM image of a rough copper surface from Ref. Vorobyev and Guo (2007a) for and [Reproduced from IOP Open Access Journal of Physics: Conference Series] (b) SEM image after digitization and conversion into a binary image which is broken up into a grid of boxes of varying size ().(c) The number of boxes () vs the box sizes () for fractal-dimension calculation.

In doing so, the SEM images of rough metal samples are first digitized in the form of binary images (black and white) as shown in Fig. ((a)a) for a rough copper taken from Vorobyev and Guo (2007a), and it is converted then into a binary image before dividing it into a grid of boxes as shown in Fig. ((b)b). The number of the boxes of size needed to cover a fractal object follows a power-law of , with being the box-counting fractal dimension. As an example, Fig. ((c)c) shows that the slope of versus on log-scale, which gives the fractal dimension . For this image, the fractional is 1.698 (solid line), which is less than a flat surface of = 2 plotted in dashed lines.

Note that in the general formulation above, we define , with . However in the characterization of 2D images, the dimension is limited to , so the fractional parameter is adjusted accordingly to be . The rough copper image of = 1.698 corresponds to = 0.698, while a perfect flat surface ( = 2) has .

We applied same procedure to calculate from SEM images of micro-scale rough surfaces reported in Refs. Vorobyev and Guo (2007a, 2005, b). The details can be found in the supplementary material. By using these values of into Eq. (18), we analytically calculate the absorptivity for copper, gold and platinum as shown in Fig. (4). From the results, we see excellent agreements between the calculations (solid lines) and experimental measurements (symbols). For each metal, the corresponding complex at =800 nm is taken from Werner et al. (2009).

In Fig. (5), we show the estimated for rough surfaces of copper Vorobyev and Guo (2007a), gold Vorobyev and Guo (2005) and platinum Vorobyev and Guo (2007b) over a wide range of laser fluence () and number of laser shots (). It is observed that for platinum at small fluence (about 0.2 ) with an increasing number of laser shots ( = 1 to 100), it is possible to have small around 0.4 to 0.7 that may enhance the absorptivity to about 20 to 40 percent [see Fig. 4c]. For copper, at =1 by increasing from 1 to 10 , absorptivity is also enhanced to more than 20 percent at = 0.66. At fixed = 1 , the roughness of copper can be increased to = 0.39 with = 2 to 3. At large 5000 with = 1 , gold can have a very rough surface with = 0.05 to 0.09, which suggest a high absorptive gold with absorptivity more than 80 percent is possible. From this analysis, it is clear that the fractal dimension is a good measure in the optimization of laser processing conditions for designing of tunable absorptive metal surfaces.

Iv Summary

The optical absorption of a rough metal surface is formulated by using a fractional model for which the absorptivity is analytically expressed as a function of to account for the surface roughness. By using the experimental results of fs laser induced roughness, we determine the values of using box-counting method on reported SEM images of rough surfaces, and the calculated absorptivity shows very good agreements for 3 studied metals: gold, copper and platinum. This model provides a fast and useful approach for experiment to create suitable surface roughness in order to tune the optical absorptivity to a value needed for many applications that require highly absorptive metallic materials. In terms of physics, this model can be seemed as a fractional model of the Fresnel coefficients. Thus it is not limited to optical absorption reported here; where it can be used for the transmission and reflection of electromagnetic waves at any rough metallic surfaces or interfaces. It is worthwhile to mention that the box-counting method can show better results on high-precision atomic force microscopic (AFM) images. We emphasize the experimentalists to directly measure the optical absorption at various rough metal surfaces and record their respective AFM images of roughness profiles. The electronic availability of such optical absorption data along with high quality AFM images would be extremely useful for further explorations and improvements in the existing theoretical model.

Figure 4: Comparison with experimentally measured absorption of (a) Cu Vorobyev and Guo (2007a), (b) Au Vorobyev and Guo (2005), and (c) Pt Vorobyev and Guo (2007b) surfaces as a function of fractal dimension parameter determined through box-counting of rough surface SEM images.
Figure 5: Calculated for rough surfaces of copper, gold and platinum at various combinations of laser fluence () and number of shots (). Each point is labeled with the calculated value and scaled in size accordingly.

V Supplementary Material

See supplementary material for the detailed fractal-dimension calculation of different rough metal (Cu, Au and Pt) surfaces using box-counting algorithm on SEM images reported in literature Vorobyev and Guo (2007a, 2005, b).

This work is supported by Singapore Temasek Laboratories (TL) seed grant (IGDS S16 02 05 1), and USA AFOSR AOARD grant (FA2386-14-1-4020).


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