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A cost-effective method for fabricating antireflective subwavelength structures on silicon carbide is demonstrated. The nanopatterning is performed in a 2-step process: aluminum deposition and reactive ion etching. The effect, of the deposited aluminum film thickness and the reactive ion etching conditions, on the average surface reflectance and nanostructure landscape have been investigated systematically. The average reflectance of silicon carbide surface is significantly suppressed from 25.4% to 0.05%, under the optimal experimental conditions, in the wavelength range of 390-784 nm. The presence of stochastic nanostructures also changes the wetting properties of silicon carbide surface from hydrophilic (47°) to hydrophobic (108°).
©2013 Optical Society of America
1. Introduction
Suppression of total internal reflection loss, arising from the abrupt refractive index difference at the interface between materials and the surroundings, is critical for many applications [1–4]. The optical performance of light-emitting diodes (LEDs) [5–8], photovoltaic devices [9] and optical lenses [10] has been improved significantly by applying antireflection coatings. A broadband suppression of reflection can be achieved by applying several layers of antireflection coatings with suitable designs [11,12]. However, such solutions are limited due to the narrow availability of materials with suitable refractive indices and well-matched thermal expansion coefficients. On the contrary, biomimetic subwavelength structures inspired by moths do not suffer such limitations and have attracted great attention.
Silicon carbide (SiC) is a wide band gap semiconductor material with excellent optical properties as well as outstanding chemical and thermal stability. Moreover, SiC can be used as an appropriate substrate for gallium nitride (GaN) growth which is the key material for high-efficiency visible [13–16] and ultraviolet [17] LEDs. Therefore, SiC is a strong candidate for solar cell applications [18], photodetectors [19] and LED devices [20]. However, SiC surfaces exhibit high reflectivity (refractive index 2.6) that limits substantially the performance of these devices. The implementation of antireflective subwavelength structures (ARS) on the SiC surface could increase drastically the efficiency of such devices.
Several technologies have been used in order to fabricate ARS, most of which deploy nano-pattern definition and etching [21–23]. Nano-pattern definition can be achieved by several techniques like electron-beam lithography [8], nanosphere lithography [24], block copolymer micelle nanolithography [25] and self assembly of noble metals [26–28]. However, such techniques make the fabrication process either expensive or time consuming, thus limit the potential for large scale applications. The need to address scalability issues led to the investigation of techniques that focus on low cost solutions. Colloidal-based rapid convective deposition method [29,30] and diblock copolymer lithography [31] had been used in order to fabricate highly-ordered micro/nano array structures with wafer scale uniformity. In this work we report a simple and cost-effective method to fabricate ARS that exhibits a stochastic landscape. The nano-pattern is formed during etching. It deploys a thin layer of aluminum (Al) which is deposited on top of a SiC surface. Al is known for its compatibility with micro/nanofabrication equipment. The stack is subsequently etched by reactive ion etching (RIE). The effect of RIE conditions and deposited Al thickness on the antireflection properties of SiC has been investigated. Moreover, the change of wetting properties of SiC, due to the presence of stochastic nanostructures is demonstrated.
2. Experiments and results
The fabrication flow of stochastic ARS is schematically illustrated in Fig. 1. A thin Al layer (20-60 nm) was deposited on the SiC surface by e-beam evaporation (Alcatel SCM600). Next, RIE (STS cluster) was performed with a mixture of CF4 and O2 gases. During RIE the Al layer is attacked by ion bombardment and is discontinuously eroded, while the SiC surface is etched, when exposed to the plasma (Fig. 1). The non-eroded Al film acts as hard masking material, together with micromasking (Fig. 2). The micromasking can be native oxide on the SiC surface or it can be generated during the process. The ion bombardment of the Al film during RIE results in sputtering, and Al nanoparticles are created. These Al nanoparticles are the main source of micromasking. Furthermore, fluorine and oxygen chemically react with Al particles and form non-volatile species that locally mask the SiC surface. The presence of fluorine and carbon can result in the formation of fluorocarbon polymer which is known to act as micromask in black silicon fabrication [32]. The residual masking is removed by using heated phosphoric acid solution and ultrasound.
Fig. 1 Schematic illustration of the stochastic SiC ARS fabrication flow (left). Schematic illustration of surface evolution during RIE due to ion bombardment, indicated by the straight blue arrows (right).
Fig. 2 An oblique-view scanning electron microscope (SEM) image of a SiC sample with ARS, before removal of the residual masking. The white arrows indicate locations with Al hard masking and micromasking respectively.
The antireflection performance of ARS depends on the landscape (height, shape and density) of surface nanostructures [33]. The landscape of nanostructures was characterized with SEM (Zeiss Supra VP 40). In order to achieve a landscape with significantly suppressed reflection, an optimization sequence has been followed. Firstly, the deposited Al thickness has been optimized, with the following fixed RIE conditions: RF power of 150 W, chamber pressure of 100 mTorr (mT), total gas (CF4 and O2) flow rate of 30 sccm, oxygen percentage 20% and process time proportional to the Al thickness. The process time was defined by thetrial and error experiments and has been fixed to the value where the hard masking was almost totally removed (Fig. 2). Secondly, the RIE parameters (power, pressure, oxygen percentage, total flow rate) have been ramped respectively and the influence of each parameter was tested in terms of average reflectance for the visible range.
The surface reflectance of bare and ARS SiC samples was measured with a calibrated goniometer system (Instrument Systems GON360) at near normal incidence (8°) for the wavelength range 390-784 nm. The receiver arm of the goniometer was connected to an optical spectrometer (Instrument Systems, CAS140B), while a halogen lamp was connected to the transmitter arm, as a broadband light source. The water contact angle measurements for characterizing the wetting properties of SiC surface were performed with a drop shape analyzer (Krüss DSA100).
2.1 Al thickness optimization
Different Al layer thicknesses are expected to provide different quantity and quality of hard masking and micromasking. As the Al thickness varies the masking equilibrium is drifted between hard masking and micromasking. Figure 3 shows the reflectance spectra and the landscape of the stochastic ARS fabricated with different deposited Al thicknesses. The average height and size of nanostructures increases with deposited Al thickness. The enlargement of nanostructure dimensions is expected as a thicker layer provides more efficient hard masking that allows longer etching. A direct correlation between final SiC landscape andinitial metal film morphology was not obtained. The average reflectance decreases from 25.42% for the bare sample to values below 2.16% for processed samples. The lowest value obtained with 60 nm deposited Al thickness is 0.36%. The combination of 15 min etching and 60 nm Al thickness that results in reflectance below 1% for the entire visible range, was considered to be optimal and was fixed for the rest experiments.
Fig. 3 Reflectance spectra for bare and ARS SiC substrates fabricated using deposited Al layers with thicknesses 20, 40 and 60 nm. The inset oblique-view SEM images show corresponding stochastic landscapes.
2.2 Impact of RIE parameters
Different RIE conditions can generate a variety of nanostructure-landscapes which lead to different optical properties. Each RIE parameter has its own significance in the way it affects the etching-masking process. Figure 4(a) shows the average reflectance of stochastic ARSfabricated with RF power in the range 120-160 W. The pressure, total flow rate and oxygen percentage were 100 mT, 30 sccm and 20% respectively. The lowest reflectance 0.05% was obtained with the RF power at 130 W. Fairly low reflectance has been achieved for the region 130-150 W, while outside this region the reflectance increases significantly. The inset SEM images show the landscape of the samples with the highest and lowest reflectance. It can be observed that the more homogeneous and dense landscape with average height around 500nm, fabricated at 130 W, results in lower reflectance.
Fig. 4 Reflectance measurements of nanostructured SiC fabricated with different a) RF power; b) pressure. The inset oblique-view SEM images show the landscape of stochastic nanostructures which correspond to the lowest (upper image) and highest (lower image) reflectance. The corresponding RIE conditions are indicated together with the achieved reflectance range and ramping step.
The pressure was altered from 80 to 120 mT, with a step of 10 mT, and the average reflectance of resulting ARS is shown in Fig. 4(b). The RF power, total flow rate and oxygen percentage were 130 W, 30 sccm and 20% respectively. It is observed that the pressure influence is relatively weak in that range, as a reflectance lower than 1% is achieved for all values. However, the landscape can change a lot, especially in terms of average structure height. For 80mT the average height of nanostructures is 200 nm, for 100 mT is 500 nm.
The presence of oxygen is known to increase the etch rate of SiC [34]. Here, the oxygen percentage was ramped from 0% to 60%. The RF power, pressure and total flow rate were 130 W, 100 mT and 30 sccm respectively. The average reflectance as a function of oxygen percentage is shown in Fig. 5(a). The presence of oxygen, as expected, is critical for achieving
Fig. 5 Reflectance measurements of nanostructured SiC fabricated with different a) oxygen percentage; b) total flow rate. The inset oblique-view SEM images show the landscape of stochastic nanostructures which correspond to the lowest (upper image) and highest (lower image) reflectance. The corresponding RIE conditions are indicated together with the achieved reflectance range and ramping step.
ARS with good antireflective performance. The intense effect, of the presence of oxygen, on the landscape is shown in the inset SEM images (Fig 5(a)). The average height of nanostructures is increased from 150 nm for 0% oxygen to 600 nm for 50%. However, the exact percentage of oxygen does not seem to have a significant impact on the reflectance properties (reflectance below 0.2% for all values). The ratio between CF4 and O2 should be vital for the balance of etching-masking chemistry and as a result crucial for the properties of ARS. However, it seems this is not the case for the specific RIE conditions.
The total flow rate of gases was varied between 20 and 40 sccm, with a step of 5 sccm. The RF power, pressure and oxygen percentage were fixed to 130 W, 100 mT and 20% respectively. The average reflectance of the corresponding ARS is shown in Fig. 5(b). It is observed that the total flow rate influence is relatively weak in that range, as a reflectance lower than 1% is achieved for all values. The landscape is also moderately affected. The average height of nanostructures increases from 350 nm to 500 nm, when the total flow rate increases from 20 sccm to 35 sccm which correspond to highest and lowest reflectance, respectively.
From the above results, it can be concluded that in order to achieve a drastic suppression of reflectance by applying stochastic nanostructures on a SiC surface, the RIE conditions need to be well tuned. Moreover, it seems that a homogeneous dense landscape, with an average height above 350 nm, can result in reflectance lower than 0.5%.
2.3 Optical and wetting properties of stochastic nanostructures
The fabrication method applied in this work results in the formation of stochastic nanostructures. The reason for that is that the masking pattern is formed simultaneously with etching, by two mechanisms, namely hard masking and micromasking. The presence of stochastic nanostructures on the SiC surface alters both its optical and wetting properties. The reflectance spectra and the water contact angle measurements shown in Fig. 6 demonstrate this effect. The reflection can be suppressed to values below 0.175% over the entire visible spectra, while the surface can be transformed from hydrophilic (47° water contact angle) to hydrophobic (108° water contact angle), corresponding to a bare surface and a stochastic ARS surface having the lowest average reflectance of 0.05%.
Fig. 6 Reflectance spectra and water contact angle measurements on bare and stochastic ARS SiC surfaces.
3. Conclusion
A simple 2-step, cost effective method for fabricating stochastic nanostructures on SiC has been demonstrated. The method takes advantage of combinatory hard masking and micromasking. The average surface reflectance can be dramatically suppressed from 25.4% to 0.05% and the wetting properties can be altered from hydrophilic to hydrophobic by applying stochastic nanostructures on the SiC surface. It was found that in order to achieve a drastic suppression of reflectance, i.e. average reflectance lower than 0.5%, the stochastic landscape should exhibit high density, high homogeneity and average height above 350nm. The three morphological characteristic were found to be equivalently important. Diverse landscapes can be fabricated by changing the initially deposited Al thickness and the RIE conditions. Although, processes that inherit stochastic characteristics are known to result in lack of reproducibility, here we report a method that allows fairly good control of the outcome at both nano and macro-scale.
Acknowledgment
The authors would like to thank the Proof of Concept funding, Danish Ministry of Science, Innovation and Higher Education for the financial support.
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