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Stereometric Parameters of the Cu/Fe NPs Thin Films Sebastian Stach, #aneta Garczyk, #tefan ##lu, Shahram Solaymani, Atefeh Ghaderi, Rostam Moradian, Negin Beryani Nezafat, Seyed Mohammad Elahi, and Hedieh Gholamali J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.5b04676 • Publication Date (Web): 03 Jul 2015 Downloaded from http://pubs.acs.org on July 5, 2015

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The Journal of Physical Chemistry

Stereometric Parameters of the Cu/Fe NPs Thin Films The running head: Stereometric Parameters of the Cu/Fe NPs Thin Films Sebastian Stach 1, Żaneta Garczyk 1, Ştefan Ţălu 2, Shahram Solaymani 3, Atefeh Ghaderi 4,*, Rostam Moradian 4, 5, Negin Beryani Nezafat 3, Seyed Mohammad Elahi 6, Hedieh Gholamali 6 1

University of Silesia, Faculty of Computer Science and Materials Science, Institute of Informatics,

Department of Biomedical Computer Systems, Będzińska 39, 41-205 Sosnowiec, Poland. 2

Technical University of Cluj-Napoca, Faculty of Mechanical Engineering, Department of AET,

Discipline of Descriptive Geometry and Engineering Graphics, 103-105 B-dul Muncii St., ClujNapoca 400641, Cluj, Romania. 3

Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Iran

4

Physics Department, Faculty of Science, Razi University, Kermanshah, Iran

5

Nano Science and Technology Research Center, Razi University, Kermanshah, Iran

6

Plasma Physics Research Centre, Science and Research Branch, Islamic Azad University, Tehran,

Iran

Corresponding author *: Atefeh Ghaderi Physics Department, Faculty of Science, Razi University, Kermanshah, Iran Phone: +989187204944 E-mail: [email protected]

Declaration of interest: The authors report no conflict of interests. The authors alone are responsible for the content and writing of the paper.

Abstract

This paper analyses the three-dimensional (3-D) surface morphology of thin films of Fe on Cu nanoparticles (NPs) synthesized by Direct-Current (DC) magnetron sputtering deposited on glass substrates. Four samples coated with copper and iron and deposited on the glass surface were used as research materials. Thin films were obtained by means of DC reactive magnetron sputtering method. The copper coating of each sample was 55 nm thick. In addition, the second, third and fourth samples had a coating of iron, with a thickness of 40 nm, 55 nm, and 70 nm, respectively. ACS Paragon Plus Environment


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The sample surface images were obtained by using an atomic force microscope operating in a contactless mode. The 3-D of the surface samples was divided into motifs of significant peaks and pits using MountainsMap® Premium software, which uses the watershed segmentation algorithm. In addition, the highest and lowest points of motifs are localized. The parameters relating to all the segmented motifs consistent with ISO 25178-2: 2012 have been generated using the software. They allow for motif analysis, detection of essential characteristics and their characterization in terms of surface dimensions, volume, curvature, shape, structure etc. MountainsMap® Premium software makes it possible to perform 3-D segmentation of sample surface images and identify all sorts of motifs, such as peaks, pits or irregular shapes in correlation with the surface statistical parameters. The analysis of motifs helps to understand their functional role in the test surface, in order to evaluate the relation among the 3-D micro-textured surface. Key words: Cu-Fe NPs, DC-magnetron sputtering, AFM, multifractal analysis, surface morphology; surface roughness.

1. INTRODUCTION

In the recent decades, considerable progress has been made in the development of theoretical and computational methods to characterize thin film microstructures at the nanometer level and thus to relate material structures to material properties.1,2 The micro morphology of thin film microstructures plays an important role in characterization of physical, chemical, and thermodynamic processes involved at nanometer scale and can bring major improvements in the functionality and quality of the analyzed product. 1,2 Surface topography is a characteristic of paramount importance in the engineering surface design based on the precision and functional performance requirements.3,4 On the other hand, the textures of most engineering surfaces are random, either isotropic or anisotropic, and either Gaussian or nonGaussian.5 Metal nanoparticles (NPs) of size less than 100 nm have been the subject of extensive research due to their unique applications in many areas.6,7 Copper oxide (CuO and Cu2O) compounds are interesting materials because of their application as catalysts, interconnects in electronic, corrosion of alloys. Also, Fe NPs are of special interest due to their possible use in magnetic recording.8 On the other hand, in literature, there are different studies about characterization of microroughness parameters of thin films prepared by DC magnetron sputtering.9-12

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Many theoretical and experimental studies highlighted that the rough surface morphology of thin films can be concisely characterized by fractal9,13,14 and multifractal15-19 geometry, which may be directly applied for data obtained from the AFM. An engineering 3-D surface with fractal/multifractal geometry has topographical features, that are independent of the measurement scale and possesses only statistical self-similarity, which takes place only in the restricted range of the spatial scales.17, 18 Our purpose in this work is to synthesis of different thicknesses of Fe on Cu NPs by Direct-current (DC) magnetron sputtering statistical parameters, and to characterize the 3-D surface morphology using AFM and the statistical parameters, in accordance with ISO 25178-2: 2012.20

2. MATERIALS AND METHODS

2.1. Materials and preparation of the thin films

In this study, four groups of six samples each - a total of 24 samples - coated with copper and iron and deposited on the glass surface were used as research materials. DC-magnetron sputtering system was applied to deposition of Cu and Cu/Fe thin films. Rotary and turbo pumps have been used for making vacuum ambient. The chamber includes two electrodes with different areas. The smaller electrode (Cu or Fe target) was attached to DC source with the radius of 8 cm; another one was grounded. The distance between electrodes was kept fix around 2 cm and glass substrates were located at ground attached electrode. The chamber was vacuumed to the pressure of 10.7×10-3 N/m2 before film deposition using rotary and turbo pumps. Then, the pressure of chamber reached 8.0 N/m2 by argon gas whereas, DC power was applied. The schematic of applied system is shown in Fig. 1.



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Fig. 1. The schematic of DC-magnetron sputtering system

Film deposition was done at room temperature as described below. At first, Cu nanoparticles were deposited at constant power of 16 Watt and basic pressure of 8.0 N/m2. The pressure measurement during deposition was done by a Pirani gauge that head was outside of plasma environment. The electrical resistivity of Cu samples was measured by simple four point probe technique. After Cu deposition, the vacuum of system was break down and Cu target replaced by Fe so, Fe NPs were added to Cu ones in three different thicknesses. Fe target has been used with 20 Watt power and previous basic pressure. The measured electrical resistivity of Cu and Cu/Fe samples was in the range of KΩ which indicates the existence of oxide layer on the samples’ surface due to reaction of surface with air ambient outside of vacuum. Thickness of Cu and Fe layer was determined by Tencor Alpha-Step 500 Profiler.21 On the whole, The Cu coating of each sample was 55 nm thick. In addition, the second, third and fourth samples had a coating of Fe, with 40 nm, 55 nm, and 70 nm respectively. The sample surface images were obtained using an atomic force microscope which operated in a contactless mode. The details of prepared samples are summarized in Table 1.

Table 1. Details and ID of prepared samples

ID

Target

Sputtering parameters Basic pressure [N/m2]

Work pressure [N/m2]

Film Power [Watt]

thickness [nm]

#1

Cu

10.7×10-3

8.0

16

55

#2

Cu/Fe

10.7×10-3

8.0

20

55/40


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#3

Cu/Fe

10.7×10-3

8.0

20

55/55

#4

Cu/Fe

10.7×10-3

8.0

20

55/70

2.2. Characterization of the thin films

The obtained samples were characterized by EDX analysis. Phase analysis was carried out by X-ray diffraction (XRD) which were recorded with CuKα (λ = 0.15406 nm) radiation as source. NPs on the surface of samples were scanned by Scanning Electron Microscopy (SEM, Philips, XL30 microscope). Characterization of film topography by atomic force microscopy was conducted in contactless mode using a Nanoscope Multimode atomic force microscope (Digital Instruments, Santa Barbara, CA), using scan rates of 10-20 µm/s to obtain 256 × 256 pixel images. The experiments were carried out at room temperature (297 ± 1 K), using cantilevers with the following nominal properties for force-distance curve measurements: length 180 µm, width 25 µm, thickness 4 µm, tip radius 10 nm, quality factor Q = 100, mass density ρ = 2330 kg/m3, Young's modulus E = 1.3 x 1011 Pa, and Poisson ratio ν = 0.28.22 All images were obtained over square areas of 1 µm x 1 µm. One typical prepared sample with highest thickness of Fe (Table 1; #4) has been used to EDX analysis. According to Fig. 2 the EDX pattern shows Fe in highest content then Cu with less content than Fe. Si peak is related to substrate.

Fig. 2. Typical EDX analysis of sample #4



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The prepared samples have been used to phase analysis by XRD. According to Fig. 3, typical XRD patterns of #1 and #4 show peaks at 37˚, 43˚ and 49˚which can be assigned to a mixture of CuO, Fe2O3 and Fe3O4, respectively. More details have been reported by our team elsewhere.23

Fig. 3 Typical XRD analysis of samples #1 and #4

In order to determine the distribution and size of NPs on the surface, typical SEM images of samples #1 and #4 was applied which is shown in Fig. 4 (a) and (b). As can be seen the mean approximate size of NPs is less than 50 nm and 70nm for #1 and #4, respectively.

(a)

(b)

Fig. 4 Typical SEM micrographs of samples (a) #1 and (b) #4

2.3. Software for motif analysis of the thin film 3-D surface In order to analyse surface images obtained by using an atomic force microscope, MountainsMap® Premium software version 6.2.7200 released on 18 September 2014 by Digital Surf was used.24 The software is designed for the measurement and analysis (according to the latest standards and ACS Paragon Plus Environment

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methods) of images acquired from different types of measuring devices, including profilometers, optical, confocal or scanning microscopes. The program includes all, even advanced, surface analysis functions. Motif analysis and the associated detection of all kinds of pits (valleys), hills or irregular shapes, take place by using watershed segmentation.

2.4. Research method of the thin film 3-D surface The obtained measurement data were loaded into the MountainsMap® Premium program (Fig. 5), and then levelled to remove the overall image slope.

Fig. 5. Surface image of the first analysed sample (group 1)

The Least Squares (LS) plane was selected as the levelling method. The levelling operation suitable for small angles of inclination, namely levelling by subtraction, was applied. Removing the least squares plane consists in calculating the equation of the P plane which minimizes the sum of the squares of the basic distances d(x,y) at the point (x,y,z), d(x,y) being the distance between the point (x,y,z) of the surface and the point (x,y,z') of the plane. This method is recommended for surfaces with random surface texture. The program includes 3-D surface stereometric parameters, as defined in ISO 25178-2: 2012 (on the surface texture), which were also generated (Table 2):20



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Table 2. 3-D surface stereometric parameters consistent with ISO 25178-2: 2012 of the first sample (group 1), for scanning square area of 1 µm x 1 µm.

The statistical parameters

Symbol

Values

Height Parameters Root mean square height

Sq [nm]

6.12

Skewness

Ssk [-]

0.190

Kurtosis

Sku [-]

2.78

Maximum peak height

Sp [nm]

21.2

Maximum pit height

Sv [nm]

19.6

Maximum height

Sz [nm]

40.8

Arithmetic mean height

Sa [nm]

4.93

Areal material ratio

Smr [%]

100

Inverse areal material ratio

Smc [nm]

8.20

Extreme peak height

Sxp [nm]

11.0

Auto-correlation length

Sal [µm]

0.030

Texture-aspect ratio

Str [-]

0.612

Texture direction

Std [°]

47.5°

Root mean square gradient

Sdq [-]

0.509

Developed interfacial area ratio

Sdr [%]

11.6

Material volume

Vm [µm³/µm²]

0.0003

Void volume

Vv [µm³/µm²]

0.0085

Peak material volume

Vmp [µm³/µm²]

0.0003

Core material volume

Vmc [µm³/µm²]

0.0056

Core void volume

Vvc [µm³/µm²]

0.0079

Pit void volume

Vvv [µm³/µm²]

0.000602

Density of peaks

Spd [1/µm²]

189

Arithmetic mean peak curvature

Spc [1/µm]

47.9

Ten point height

S10z [nm]

28.0

Functional Parameters

Spatial Parameters

Hybrid Parameters

Functional Parameters (Volume)

Feature Parameters


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Five point peak height

S5p [nm]

15.8

Five point pit height

S5v [nm]

12.2

Mean dale area

Sda [µm²]

0.00671

Mean hill area

Sha [µm²]

0.00502

Mean dale volume

Sdv [µm³]

2.52e-006

Mean hill volume

Shv [µm³]

5.8e-006

Next a 3-D view of the analysed sample surface (group 1) was created including elements such as scale, axis arrangement; dimensions block (Fig. 6):

Fig. 6. 3-D view of the first analysed sample surface (group 1)

The test surface peaks were detected by means of motif analysis. In order to reduce the number of detected local peaks resulting from noise, a smoothing filter sized 3×3 was applied. Furthermore, a minimum height of motifs was set at 0.65% of Sz – the motifs that do not meet this criterion are automatically combined with their neighbours.

Parameters relating to all the motifs were also generated. The parameters included the number of motifs, mean height, mean area, mean volume, mean perimeter, mean of equivalent diameters, mean of mean diameters, mean of minimum and maximum diameters, mean of minimum and maximum diameter angles, mean form factor, mean aspect ratio, mean roundness, mean compactness, mean orientation, mean sphere radius (Table 3 and Fig. 7).



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Fig. 7. Result of segmentation performed to detect the peaks of the first sample surface (the cross locates the highest motif point)

Graphical representation of motifs enables to choose from among several types of visualization: - Interactive visualization of motifs, allowing the user to select a motif with a mouse (Fig. 8, a); - Visualization of motifs by means of coloured cells (Fig. 8, b); - Visualization of motifs on monochrome background (Fig. 8, c); - Visualization of spherical caps included in motifs (Fig. 8, d); - Display of open motifs (Fig. 8, e) - which are on the image edge, or closed motifs (Fig. 8, f) – which do not touch the image edge; - Visualization of image motifs together with the number of each motif (Fig. 8, g).


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a)

b)

c)

d)



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e)

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f)

g) Fig. 8. Display modes: a) visualisation of an interactively selected motif (peak), b) visualisation of peaks by means of coloured cells, c) visualisation of peaks on monochrome background, d) visualization of spherical caps included in motifs (peaks), e) display of open motifs (peaks), f) display of closed motifs (peaks), g) visualisation of peaks together with the number of each motif.

Motif analysis was also applied in order to detect pits and surface shapes. Both in the first and the second case, a smoothing filter sized 3×3 was used. The minimum height of motifs for pit detection was set at 1.4% of Sz, whereas for shape detection it was 5% of Sz.


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Furthermore, parameters relating to all detected pits (Fig. 9, Table 4) and then shapes (Fig. 10, Table 5) were generated, as in the case of peak detection.

Fig. 9. Result of segmentation performed in order to detect surface pits of the first sample (group 1) (the cross locates the lowest motif point)

Fig. 10. Result of segmentation performed in order to detect surface shapes of the first sample ACS Paragon Plus Environment


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(group 1)

The other three samples of three groups were analyzed. As a result, the image of the second sample surface (group 2) was levelled, and its three-dimensional view and identification card of the sample were created (Fig. 11).

a)

b) Fig. 11. Second sample (group 2): a) levelled image of the sample surface, b) 3-D view of the sample surface

The analysed surface was divided into motifs using a segmentation algorithm which took into account the pre-selected filtering and thresholding. The detection of specific points uses a watershed algorithm. All peaks and hollows and their associated zones are identified, then the segmentation ACS Paragon Plus Environment

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method is applied. The result is expressed as a percentage of the total height of the surface or as a percentage of the area of the surface. The segmentation method is used for determining what peaks are significant for the calculation of the feature parameters. Segmentation takes into account user-selected filtering and thresholding or pruning criteria. Filtering (in the range 3x3 to 13x13) is used to reduce very local peak/pit detection. Segmentation is controlled by minimum height and minimum area criteria for motifs. Parameters relating to all the detected motifs were also generated (Fig. 12).

a)



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b)

c)

Fig. 12. Second sample (group 2): a) result of segmentation of surface peaks (the cross locates the highest motif point) together with parameters relating to all the generated motifs, b) result of segmentation of surface pits (the cross locates the lowest motif point) together with parameters relating to all the generated motifs, c) result of segmentation of surface shapes together with ACS Paragon Plus Environment

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parameters relating to all the generated motifs

In analogy to the second sample (group 2), also for the third one (group 3) there followed automated analysis of the surface image, which was initially levelled with the use of the previously created template. An identification card and a three-dimensional view of the sample surface were also created (Fig. 13).

a)

b) Fig. 13. Third sample (group 3): a) levelled image of the sample surface, b) 3-D view of the sample ACS Paragon Plus Environment


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surface

Using the segmentation algorithm, which took into account the pre-selected filtering and thresholding, the third analysed surface (group 3) was divided into motifs - peaks, pits and shapes. At the same time, parameters for all detected motifs were generated (Fig. 14).

a)

b) ACS Paragon Plus Environment

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c)

Fig. 14. Third sample (group 3): a) result of segmentation of surface peaks (the cross locates the highest motif point) together with parameters relating to all the generated motifs, b) result of segmentation of surface pits (the cross locates the lowest motif point) together with parameters relating to all the generated motifs, c) result of segmentation of surface shapes together with parameters relating to all the generated motifs

The template was also used for the fourth sample (group 4), which resulted in automatically generated analysis as in the case of the previous three samples, (Fig. 15) and (Fig. 16).



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a)

b) Fig. 15. Fourth sample (group 4): a) levelled image of the sample surface, b) 3-D view of the sample surface


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a)

b)



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c) Fig. 16. Fourth sample (group 4): a) result of segmentation of surface peaks (the cross locates the highest motif point) together with parameters relating to all the generated motifs, b) result of segmentation of surface pits (the cross locates the lowest motif point) together with parameters relating to all the generated motifs, c) result of segmentation of surface shapes together with parameters relating to all the generated motifs

3. ANALYSIS OF RESULTS

The 3-D surface of the samples is divided into motifs of significant peaks and pits using MountainsMap® Premium software, which uses the watershed segmentation algorithm. In addition, the highest and lowest points of motifs are localized. The boundary of generated motifs is marked by the course line (peaks) or ridge line (pits). In the case of peaks the highest point of the course line is called a saddle point, whereas in the case of pits this term refers to the lowest point on the ridge line. The course line intersects with the ridge line at the saddle point. The applied software also provided parameters relating to the generated peaks (Table 3).

Table 3. Parameter values for different measurement data resulting from segmentation of surface peaks


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Research material #1

#2

#3

#4

Parameter Number of motifs

299

116

87

105

Mean height [nm]

3.609001

1.682311

2.18305

2.325208

Mean area [µm2]

0.003371

0.008688

0.011585

0.009599

Mean volume [nm3]

2834.02

5112.314

6024.524

6449.944

Mean perimeter [µm]

0.230649

0.391886

0.464005

0.413302

Mean of equivalent diameters [µm]

0.05763

0.091142

0.108527

0.097491

Mean of mean diameters [µm]

0.056053

0.08677

0.10315

0.092595

Mean of minimum diameters [µm]

0.039439

0.056187

0.066847

0.060317

Mean of maximum diameters [µm]

0.082419

0.139858

0.171287

0.153613

Mean of minimum diameter angles [°]

-16.3813

-19.7328

-22.6322

-29.5048

Mean of maximum diameter angles [°] -20.495

-15.1983

-15.1379

-15.781

Mean form factor [µm]

0.633654

0.559151

0.545508

0.558146

Mean aspect ratio [µm]

2.235755

2.772685

3.004842

2.978758

Mean roundness [µm]

0.510657

0.450251

0.419368

0.421268

Mean compactness [µm]

0.708964

0.664036

0.637997

0.639182

Mean orientation [µm]

62.94432

65.10739

56.97829

58.48253

Mean sphere radius [µm]

2834.02

5112.314

6024.524

6449.944

The number of segmented peaks is the highest on the first sample surface and is 299. The number of motifs on the second sample surface is by more than half smaller, as there are only 116 peaks - 11 more than on the fourth sample surface. The lowest number of peaks, namely 87, was found on the third sample surface. The mean value of the peak height, which is the distance between the saddle point and the peak, is the largest in the first sample (3.609001 nm), and the smallest in the second sample (1.682311 nm). Whereas the mean horizontal surface enclosed by the course line, which is the mean area of the motif (peak), is the largest in the case of the third sample (0.011585 µm2), and the smallest for the first sample (0.003371 µm2). The fourth sample has the highest mean peak material volume (calculated above the highest saddle point) which is 6449.944 nm3, in contrast to the first sample, wherein the mean volume is a bit more than twice smaller (2834.02 nm3). The highest mean perimeter of the motif is 0.464005 µm in the case of the third sample, and the smallest 0.230649 µm (first sample). Similarly, the mean of equivalent diameters, which is the mean diameter value of the disk whose surface area is equal to



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the peak area, has the highest value for the third sample (0.108527 µm), and the smallest for the first sample (0.05763 µm). The third sample also has the highest mean value of mean diameters (0.10315 µm), the highest mean of the smallest (0.066847 µm) and largest (0.171287 µm) grain diameters, which are measured from the centre of gravity, in contrast to the first sample for which these parameters have minimum values (the mean of mean diameters: 0.056053 µm, mean of minimum diameters: 0.039439 µm, mean of maximum diameters: 0.082419 µm). The mean angle of the smallest and largest peak diameters is measured from its centre of gravity (assuming: 0°- on the right, 180° - on the left, 90°- on top, 90° - at the bottom). The greatest value of the mean angle of the smallest motif diameter is for the fourth sample (-29.5048°), and the smallest for the first one (-16.3813°), which at the same time has the highest value of the mean angle of the largest motif diameter, namely 20.495°. This parameter is similar for the other three samples. However, it has the smallest value in the case of the third sample (-15.1379°). The mean ratio of the grain surface to the perimeter squared is called the mean form factor. This parameter has the highest value, namely 0.633654 µm, in the first sample, and the smallest, equal to 0.545508 µm, in the third sample. The other two samples have a similar mean form factor. The mean roundness is a parameter defining the mean ratio of the grain surface to the disk surface with a maximum grain diameter. In contrast, the mean compactness is the mean ratio of the equivalent diameter to the maximum diameter. For both parameters, a value close to 1 means a round grain and smaller than 0.5 an elongated one. The parameters are the greatest for the first sample and are 0.708964 µm and 0.510657 µm respectively. However, they are not close to one. Therefore it is impossible to talk about disk-shaped motifs. The other samples have similar values of these two parameters. The lowest mean roundness (0.419368 µm) and compactness (0.637997 µm) are in the third sample, which is thus characterized by the most elongated motifs. It is also confirmed by the highest mean aspect ratio of 3.004842 µm which was calculated for this sample. This parameter is the mean ratio of the maximum diameter to the minimum one and for the motifs similar in shape to a disk it has a value close to one. A high value of the parameter indicates an elongated motif. The first sample has the lowest mean aspect ratio (2.235755 µm). The mean value of the angle between 0° and 180° of the largest grain axis, measured in the trigonometric direction, is called the mean orientation. The highest value of this parameter is characteristic for the second sample (65.10739 µm), and the lowest for the third one (56.97829 µm). The last parameter is the mean sphere radius, which is the highest for the fourth sample (6449.944 µm) and the lowest for the first one (2834.02 µm).

The parameters related to the surface pits were also obtained using the software (Table 4).


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Table 4. Parameter values for individual measurement data resulting from segmentation of surface pits Research material #1

#2

#3

#4


211

70

67

86

Mean height [nm]

3.116102

2.489303

2.392511

2.416624

Mean area [µm2]

0.004777

0.014398

0.015043

0.011719

Mean volume [nm3]

1651.386

4013.074

3198.09

2186.18


0.290178

0.537008

0.561974

0.492793


0.069222

0.120185

0.125858

0.110918


0.067285

0.115181

0.121339

0.105662


0.046966

0.078431

0.084402

0.070173


0.099173

0.178319

0.188177

0.170104


-11.7441

-24.5714

-12.9701

-22.6824

Mean of maximum diameter angles [°] -17.8199

-14.6

-11.6716

-7.89412


0.576452

0.539618

0.503463

0.520452


2.248305

2.346836

2.461596

2.659992


0.510365

0.494605

0.461602

0.447469


0.708742

0.696937

0.671361

0.656717


67.82751

74.09774

74.519

73.8368


1651.386

4013.074

3198.09

2186.18

Segmentation of pits provided results similar to the ones obtained for peak segmentation. Also here the highest number of motifs was identified on the first sample surface, namely 211, and the lowest on the third sample surface, only 67. The fourth sample had 86 pits - 16 more than the second sample. The mean pit height, which is the distance between the pit and the saddle point, is the largest in the first sample (3.116102 nm), and the lowest in the third one (2.392511 nm). In contrast, the mean pit area is the largest for the third sample (0.015043 µm2) - as in the case of peak segmentation – and the smallest in the first sample (0.004777 µm2). The second sample has the highest mean pit material volume of 4013.074 nm3, in contrast to the first sample, wherein the mean volume is much smaller (1651.386 nm3). The highest mean perimeter of the motif is 0.561974 µm (third sample), and the smallest 0.290178 µm (first sample). Similarly, the mean of equivalent diameters is the greatest for the third sample (0.125858 µm), and the smallest for the first sample



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(0.069222 µm). The third sample also has the highest mean of mean diameters (0.121339 µm), and the highest mean of the smallest (0.084402 µm) and highest (0.188177 µm) grain diameters, in contrast to the first sample for which these parameters have the lowest values (the mean of mean diameters: 0.067285 µm, the mean of minimum diameters: 0,046 966 µm, the mean of maximum diameters: 0.099173 µm). The second sample has the highest mean angle value of the smallest motif diameter (-24.5714°), whereas the lowest value belongs to the first sample (-11.7441°), which at the same time has the highest mean angle value of the largest motif diameter (-17.8199°). This parameter value is the smallest in the case of the fourth sample (-7.89412°). The biggest mean form factor is in the first sample - 0.576452 µm, and the smallest in the third one - 0.503463 µm. The mean roundness and compactness have the highest values in the case of the first sample and they are 0.510365 µm and 0.708742 µm respectively. However, these parameters are not close to one. Therefore it is impossible to talk about disk-shaped motifs. The lowest mean roundness (0.447469 µm) and compactness (0.656717 µm) belong to the fourth sample, which is characterized by the most elongated motifs. This is also confirmed by the highest mean aspect ratio of 2.659992 µm for this sample. The first sample has the lowest mean aspect ratio (2.248305 µm). The highest mean orientation is characteristic for the third sample (74.519 µm), and the smallest for the first sample (67.82751 µm). The mean sphere radius is the biggest for the second sample and is 4013.074 µm, and the lowest for the first sample (1651.386 µm). Parameters related to surface shapes were also generated (Table 5).

Table 5. Parameter values for individual measurement data resulting from segmentation of surface shapes Research material #1

#2

#3

#4


1120

353

381

385

Mean height [nm]

17.23272

11.29352

12.55498

14.761

Mean area [µm2]

899.8737

0.002855

0.002645

0.002618

Mean volume [nm3]

374.6147

691.4095

439.6857

628.0076


0.113581

0.224391

0.214706

0.209599


0.02783

0.048224

0.046631

0.045312


0.02692

0.044549

0.043186

0.041725


0.01667

0.025562

0.024898

0.023335


0.041583

0.082386

0.078977

0.078339


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-22.5866

-31.8017

-34.0892

-34.5156

Mean of maximum diameter angles [°]

-21.2777

-16.9207

-17.2336

-17.0339


0.616344

0.511928

0.515398

0.51195


2.922736

3.853182

3.916145

4.344827


0.468153

0.387874

0.394129

0.379211


0.677076

0.612363

0.616215

0.601741


71.7957

68.64829

67.91092

64.2341


374.6147

691.4095

439.6857

628.0076

The highest number of motifs (1120) was segmented on the first sample surface, whereas the fewest motifs, only 353, were identified on the second sample surface. The fourth sample has 385 shapes 4 more than the third sample surface. The mean shape height is the largest in the first sample (17.23272 nm), and the smallest in the second sample (11.29352 nm), whereas the mean area of the shape for the first sample differs significantly from the values of this parameter for the other samples and is 899.8737 µm2. The mean shape area of the other samples is similar. However, the lowest value of this parameter is in the case of the fourth sample (0.002618 µm2). The second sample is characterized by the highest mean shape material volume, which is 691.4095 nm3, in contrast to the first sample, in which the mean volume is much smaller (374.6147 nm3). The largest mean motif perimeter is 0.224391 µm in the case of the second sample, and the smallest 0.113581 µm (first sample). Similarly, the mean of equivalent diameters is the highest for the second sample (0.048224 µm), and the lowest for the first one (0.02783 µm). The second sample also has the greatest mean value of mean diameters (0.044549 µm), and the highest mean of the smallest (0.025562 µm) and largest (0.082386 µm) grain diameters. The first sample, on the other hand, has the lowest values of these parameters (the mean of mean diameters: 0.02692 µm, the mean of minimum diameters: 0.01667 µm, the mean of maximum diameters: 0.041583 µm). The fourth sample has the highest mean angle value of the smallest motif diameter (34.5156°), and the lowest value belongs to the first sample (-22.5866°), which at the same time has the highest mean angle value of the largest motif diameter (-21.2777°). This parameter value is the smallest in the case of the fourth sample (-17.0339°). The first sample has the highest mean form factor (0.616344 µm), whereas for the second and fourth samples this parameter is similar and, at the same time, the lowest (0.511928 µm – second sample, 0.51195 µm – fourth sample). The mean roundness and compactness have the highest values in the case of the first sample and they are 0.468153 µm and 0.677076 µm respectively. However, these parameters are not close to one. Therefore it is impossible to talk about disk-shaped motifs. The lowest mean roundness (0.379211 µm) and



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compactness (0.601741 µm) belong to the fourth sample, which is therefore characterized by the most elongated motifs. This is also confirmed by the highest mean aspect ratio of 4.344827 µm for this sample. The first sample has the lowest mean aspect ratio (2.922736 µm). The highest mean orientation is also characteristic for the first sample (71.7957 µm), and the smallest for the fourth one (64.2341 µm). The mean sphere radius is the biggest for the second sample and is 691.4095 µm, and the lowest for the first sample (374.6147 µm).

4. DISCUSSION

AFM in combination with statistical surface parameters characterize changes in the spatial distribution of multi-components in thin films morphology and topography that occur at the micrometer- and nanometer-scale. The extraction procedure is not very time consuming. The first sample, i.e. the sample with the copper coating having a thickness of 55 nm deposited on the glass surface, is characterized by the largest number and height of segmented motifs, both peaks and pits. The sample with the copper coating having a thickness of 55 nm and iron coating of the same thickness deposited on the glass surface, namely the third sample, has the fewest segmented motifs and the lowest height of pits. In addition, they have the largest area, perimeter, equivalent diameters, mean diameters and the smallest and largest motif diameters. It can therefore be concluded that the first sample surface has the most irregular topography, in contrast to the third sample, which, compared to the other three samples, has the most regular surface. However, both peaks and pits on the first sample surface are characterized by the smallest aspect ratio (highest roundness). The third sample, on the other hand, has the most elongated peaks, and the fourth sample (55 nm thick Cu layer, 70 nm thick Fe layer) - pits. In addition, it can also be noted that in the case of all samples the number of segmented peaks is larger than the number of pits. A significant correlation (P < 0.05) is observed and indicates that applied method is also correct in the analysis on samples 3-D morphology. Furthermore, the 3-D morphology pattern of nanostructures for the samples of thin films is estimated by specific parameters that can be included in mathematical models to characterize local morphology of the surfaces more accurate.

5. CONCLUSIONS

This work presents Cu NPs prepared by DC magnetron sputtering system with 40 nm, 55 nm and 70 nm thickness of Fe over layer. The characterizations were carried out using EDX and AFM analyses. Using AFM data the 3-D morphology pattern of the samples were investigated. The objective of the study has been achieved and it has been proven that MountainsMap® Premium ACS Paragon Plus Environment

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software enables to perform 3-D segmentation of sample surface images and identify all sorts of motifs, such as peaks, pits or irregular shapes. In addition, parameters relating to all the segmented motifs consistent with ISO 25178-2: 2012 have been generated using the software. They allow for motif analysis, detection of essential characteristics and their characterization in terms of surface dimensions, volume, curvature, shape, structure, etc. The analysis of motifs helps to understand their functional role in the test surface. The analyzed images, acquired using an atomic force microscope, show the surface of the four samples with applied copper and iron films of varying thickness deposited on the glass surface. Based on the performed tests and owing to the analysis of parameters relating to all the segmented motifs, it can be concluded that the test samples are characterized by a non-uniform surface, and thus the iron layer thickness influences the surface morphology. MountainsMap® Premium software was used to characterize surface topography in a new way, which is closely connected with material properties and enables to localize defects with maximum interclass variance and minimum intra class variance. Therefore, 3-D surface morphology analysis is extremely important in the engineering design of thin film surfaces, synthesised by (DC) magnetron, surfaces based on precise and functional operating requirements.

ACKNOWLEDGEMENTS

The authors are grateful to thank Mrs S. Vaseghinia for the AFM measurements.

REFERENCES

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(19) Ţălu, S.; Stach, S.; Mahajan, A.; Pathak, D.; Wagner, T.; Kumar, A.; Bedi, R. K. Multifractal Analysis of Drop-Casted Copper (II) Tetrasulfophthalocyanine Film Surfaces on The Indium Tin Oxide Substrates. Surf. Interface Anal. 2014, 46, 393−398. (20) ISO 25178−2:2012, Geometrical Product Specifications (GPS) - Surface Texture: Areal – Part 2: Terms, Definitions and Surface Texture Parameters; Available from: http://www.iso.org (accessed June 10th, 2015). (21) Ghaderi, A.; Elahi, S.M.; Solaymani, S.; Naseri, M.; Ahmadirad, M.; Bahrami, S.; Khalili, A.E.; Thickness Dependence of the Structural and Electrical Properties of ZnO Thermal-Evaporated Thin Films. PRAMANA J. Phys. 2011, 77, 1-8. (22) Bhushan, B.; Fuchs, H.; Kawata, S. Applied Scanning Probe Methods V; Springer: Heidelberg, Germany, 2007. (23) Ţălu, Ş.; Stach, S. ; Solaymani, S; Moradian, R.; Ghaderi, A.; Hantehzadeh, M.R.; Elahi, S.M.; Garczyk, Ż.; Izadyar, S.; Multifractal Spectra of Atomic Force Microscope Images of Cu/Fe Nanoparticles Based Films Thickness. J. ele. Chem. 2015, 749, 31–41. (24) Mountains Map® 7 Software (Digital Surf, Besançon, France). Available from: http://www.digitalsurf.fr (last accessed June 10th, 2015).



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Fe NPs Thin Films - The Journal of

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